{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>BLKGRPCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>051</td>\n",
       "      <td>070100</td>\n",
       "      <td>1</td>\n",
       "      <td>270510701001</td>\n",
       "      <td>Block Group 1</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>17698490</td>\n",
       "      <td>1460274</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>POLYGON ((-96.00778 45.97887, -96.00778 45.979...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>051</td>\n",
       "      <td>070200</td>\n",
       "      <td>3</td>\n",
       "      <td>270510702003</td>\n",
       "      <td>Block Group 3</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>27137279</td>\n",
       "      <td>586847</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON ((-95.84144 45.81994, -95.84144 45.820...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>051</td>\n",
       "      <td>070200</td>\n",
       "      <td>1</td>\n",
       "      <td>270510702001</td>\n",
       "      <td>Block Group 1</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>359261041</td>\n",
       "      <td>16334586</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON ((-96.13048 45.86130, -96.12940 45.861...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>051</td>\n",
       "      <td>070100</td>\n",
       "      <td>2</td>\n",
       "      <td>270510701002</td>\n",
       "      <td>Block Group 2</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>498024195</td>\n",
       "      <td>43000411</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>POLYGON ((-96.26618 46.02237, -96.26614 46.024...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>053</td>\n",
       "      <td>026508</td>\n",
       "      <td>2</td>\n",
       "      <td>270530265082</td>\n",
       "      <td>Block Group 2</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>1788814</td>\n",
       "      <td>819265</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-93.44624 45.05509, -93.44624 45.055...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 346 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20 BLKGRPCE20       GEOID20     NAMELSAD20  \\\n",
       "0        27        051    070100          1  270510701001  Block Group 1   \n",
       "1        27        051    070200          3  270510702003  Block Group 3   \n",
       "2        27        051    070200          1  270510702001  Block Group 1   \n",
       "3        27        051    070100          2  270510701002  Block Group 2   \n",
       "4        27        053    026508          2  270530265082  Block Group 2   \n",
       "\n",
       "  MTFCC20 FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004  \\\n",
       "0   G5030          S   17698490   1460274  ...        0        0        0   \n",
       "1   G5030          S   27137279    586847  ...        0        0        0   \n",
       "2   G5030          S  359261041  16334586  ...       39        0        0   \n",
       "3   G5030          S  498024195  43000411  ...        0        0        0   \n",
       "4   G5030          S    1788814    819265  ...        0        0        0   \n",
       "\n",
       "  P0050005 P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0       17        0        0       17   \n",
       "1        0        0        9        0        0        9   \n",
       "2       39        0        9        0        0        9   \n",
       "3        0        0       15        0        0       15   \n",
       "4        0        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-96.00778 45.97887, -96.00778 45.979...  \n",
       "1  POLYGON ((-95.84144 45.81994, -95.84144 45.820...  \n",
       "2  POLYGON ((-96.13048 45.86130, -96.12940 45.861...  \n",
       "3  POLYGON ((-96.26618 46.02237, -96.26614 46.024...  \n",
       "4  POLYGON ((-93.44624 45.05509, -93.44624 45.055...  \n",
       "\n",
       "[5 rows x 346 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/mn_pl2020_bg.dbf\") #usually use tracts, but use block groups for this special run\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e4af1be-0f6a-4c46-9d23-da70d63c30e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 4706 popn tracts for MN\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"MN\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population MN voter-age population\n",
      "state pop= 5706494 VAP pct Hispanic is  0.04984400891950459\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP MN\n",
      "total state pop= 5706494 , VAP pct Black is  0.0593374440337997\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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xjiSfw7oB0xFQLqs0nmPA/mF7P/DgSPu+JNcn2Q7sAE4OlxV+luT2YTbQn488ZioNf+engTNV9bGRQ9ZuFUnmkrx+2H4N8E7g+1i7FVXVwaraWlXzLP7b9ZWqei/WbdFGz9JYjxvwLhZnXP0A+MhG92ejb8AXgPPA/7D4zuoe4NeBE8CTw/2NI+d/ZKjdWUZm/gA7gceHY59gWHlkWm/AH7J4WeS7wGPD7V3Wbqza/Q7w7aF2jwN/M7Rbu/Fr+Hb+fxafdatyqSNJUk/TcIlPkjSFDChJUksGlCSpJQNKktSSASVJasmAkiS1ZEBJklr6X7pZTrngpA/zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n"
     ]
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "\n",
    "lakeTracts = [0]\n",
    "minTractPop = 20\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 999 and x > -999 : #optional to exclude some areas from plotting\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y+0.00,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if y > 9999:  #for Tennessee, these ALL appear to be interior\n",
    "            if lakeTracts == [0]:\n",
    "                lakeTracts = [m]\n",
    "                isSkippedTract[m] = 1\n",
    "            else:\n",
    "                if y > 34:  #m==99940 or m==999909: \n",
    "                    thisTract = \"interior\"\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "greatLakeTracts = [804,1509,3582]\n",
    "for gLT in range(len(greatLakeTracts)):\n",
    "    t = greatLakeTracts[gLT]\n",
    "    isSkippedTract[t]=1\n",
    "    plotPoly(tractGeom[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4706 3\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))  #confirm our skips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VTDID</th>\n",
       "      <th>PCTNAME</th>\n",
       "      <th>COUNTYNAME</th>\n",
       "      <th>COUNTYFIPS</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREAFUE</th>\n",
       "      <th>G20PREPLAR</th>\n",
       "      <th>G20PRESKEN</th>\n",
       "      <th>G20PREIWES</th>\n",
       "      <th>G20PREIPIE</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>G20USSDSMI</th>\n",
       "      <th>G20USSRLEW</th>\n",
       "      <th>G20USSMOCO</th>\n",
       "      <th>G20USSCSTE</th>\n",
       "      <th>G20USSOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>271730045</td>\n",
       "      <td>Friendship Twp</td>\n",
       "      <td>Yellow Medicine</td>\n",
       "      <td>173</td>\n",
       "      <td>26</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>95</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-95.72666 44.80511, -95.72658 44.790...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>270910110</td>\n",
       "      <td>Galena Twp</td>\n",
       "      <td>Martin</td>\n",
       "      <td>091</td>\n",
       "      <td>40</td>\n",
       "      <td>91</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-94.70471 43.84815, -94.70470 43.846...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>270930045</td>\n",
       "      <td>Darwin Twp</td>\n",
       "      <td>Meeker</td>\n",
       "      <td>093</td>\n",
       "      <td>129</td>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>291</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-94.37896 45.15235, -94.37891 45.137...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>271370060</td>\n",
       "      <td>Biwabik Twp</td>\n",
       "      <td>St. Louis</td>\n",
       "      <td>137</td>\n",
       "      <td>249</td>\n",
       "      <td>335</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>316</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>MULTIPOLYGON (((-92.41044 47.49827, -92.41296 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>270010015</td>\n",
       "      <td>Ball Bluff Twp</td>\n",
       "      <td>Aitkin</td>\n",
       "      <td>001</td>\n",
       "      <td>75</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>95</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-93.19114 47.02518, -93.19113 47.025...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VTDID         PCTNAME       COUNTYNAME COUNTYFIPS  G20PREDBID  \\\n",
       "0  271730045  Friendship Twp  Yellow Medicine        173          26   \n",
       "1  270910110      Galena Twp           Martin        091          40   \n",
       "2  270930045      Darwin Twp           Meeker        093         129   \n",
       "3  271370060     Biwabik Twp        St. Louis        137         249   \n",
       "4  270010015  Ball Bluff Twp           Aitkin        001          75   \n",
       "\n",
       "   G20PRERTRU  G20PRELJOR  G20PREGHAW  G20PREAFUE  G20PREPLAR  G20PRESKEN  \\\n",
       "0          96           1           2           1           0           0   \n",
       "1          91           2           0           0           0           0   \n",
       "2         310           1           0           2           0           0   \n",
       "3         335           1           1           1           0           0   \n",
       "4         107           3           0           0           0           0   \n",
       "\n",
       "   G20PREIWES  G20PREIPIE  G20PREOWRI  G20USSDSMI  G20USSRLEW  G20USSMOCO  \\\n",
       "0           0           1           0          27          95           4   \n",
       "1           0           0           0          46          80           4   \n",
       "2           0           1           0         131         291          12   \n",
       "3           4           1           1         240         316          19   \n",
       "4           0           0           0          79          95           6   \n",
       "\n",
       "   G20USSCSTE  G20USSOWRI                                           geometry  \n",
       "0           2           0  POLYGON ((-95.72666 44.80511, -95.72658 44.790...  \n",
       "1           3           0  POLYGON ((-94.70471 43.84815, -94.70470 43.846...  \n",
       "2           3           0  POLYGON ((-94.37896 45.15235, -94.37891 45.137...  \n",
       "3          11           0  MULTIPOLYGON (((-92.41044 47.49827, -92.41296 ...  \n",
       "4           1           0  POLYGON ((-93.19114 47.02518, -93.19113 47.025...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/mn_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4110 0.46360486351433333 3201142 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 300\n",
      "working on tract 600\n",
      "working on tract 900\n",
      "working on tract 1200\n",
      "working on tract 1500\n",
      "working on tract 1800\n",
      "working on tract 2100\n",
      "working on tract 2400\n",
      "working on tract 2700\n",
      "working on tract 3000\n",
      "working on tract 3300\n",
      "working on tract 3600\n",
      "working on tract 3900\n",
      "working on tract 4200\n",
      "working on tract 4500\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic MN map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%300 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-92.,46)\n",
    "pt2 = Point(-89,46)\n",
    "pt3 = Point(-89,49.1)\n",
    "pt4 = Point(-93.6,49.1)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  194 tracts w pop 191282.0  to reassign to tracts...\n",
      "[93, 239, 1400, 1401, 1404, 1406, 1410, 1412, 1413, 1414, 1415, 1417, 1419, 1487, 1506, 1560, 1664, 1920, 1971, 3113, 3116, 3117, 3868, 3946, 4256, 4257, 4258, 4259, 4260, 4422, 4423, 4424, 4425, 4426]\n",
      "I have flagged  170 precincts to reassign to precincts...\n",
      "[228, 270, 410, 605, 657, 714, 847, 1203, 1210, 1345, 1873, 1931, 2112, 2192, 2578, 2763, 2764, 2765, 2766, 3966, 3967, 3968, 3969, 3970, 4022, 4024]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N9xu7D3uMmpuOIzqv/AjelclnQ/n6XwZcJiLLgRhQKiIPAR8haLcHeAS4P0PZHcDMAa9nkLmJx5i8J65D4eLavtfFp08jubuL9hXvE3+vDbckQtHSKXT+qYnd976BhJzgwq8EzxUuPKmW4jOmBbX43sc+atCtuO2P7xHf2ErnK42W6M1BDpvoVfUO4A6AdI3+NlW9XkTWAecAK4HzgY0Zir8KHCUic4F64Brgk6MRuDH5IFxTSOVVi/ZbVrR0Cp2vNAIQmVlCbFFl3wPkB1Nz43Hs/N6rYxanmdhG0qD3eeAHIhICeki3r4vINOB+VV2uqikRuRV4EnCBn6nq2yMN2ph8Fq4rovwj87Mdhskjw0r0qrqSoAaPqj4PnJxhmwZg+YDXjwGPjSTIYbHWfWOM2U/+dQT205nezb+3ZowxRyLv+mKpF9y1aP2Tx4emfLz2BPiK+pqeEnzh+oqm/GCogHSXwKBrYLpwbxfBvuXpFb390fef9G/jCOIMmO/tZugIIhJUX0TsAeDGpOVfok8FacES/fho+cU6etbtyXYYg+v9Qhj4JdP7BaMekuigf5AkTc8rkp4GX0oDlw/cDvZrK5T0+r4n1A8sT//2Az6afduL7rdQBi7r+2LsPf6Awn3bgt82Eyq6gA8M6xSZ/Jd3id6absaXty9OeGoRxWdOD75cnXRN2hE06QfLJJ30dMAIo8qAZQT/8dn/zouBtXwI/t+mt1PV/l8N2r+u79eEpn9hpNdp7/H89L5UYfMKdPeG4EBVC/sPrr3hyYB8Ln3FBr7u01tmwD72mx6wXMmwvm+sJgm+UtTZ7/h6wHZBDNIXq6uNRP16gs5wxvTLu0Sv6URvP9vHiQ9uZYyik+uyHcnwfe0kCAPHXA5XfSfb0Yzcd+bC9I9nOwqTg/Kv2uvr4bcxo0ZVg/byiWjW6cH0qgezG8doSXRCuDDbUZgclHc1+t5fyLv/5a3gAt3A9tFM3wEH/4bOuJ0Ocbv9lx34Oz0Lhv3DZpgFfCU8pWi4B8kNtUdD0zroaYNY6eG3z2W+B14cIhP0/4UZU3mX6KNzyyg5dwaa8PsXZspdmUafkgOmB76QQ60+eLuM+x5P4/EFo1BwXPU4HGgMFE+Bnla4exH82bfg5E9P3KfWJDqDqSV6k0HeJXonFqLsw3OzHYaZCM6+DWafAc9+D373Jdj03/CRH0JRVbYjG77eRG9NNyaDvEv0xgyZ48Lcs2D2Mnj5R/Dfd8L35oEbIT32L+kO+wfMM8jyoWzv0NfPs3c+VgbX/BKKRvDLKNkVTK1GbzKwRG+M48AZfwnzz4e3HwU/Ber39vtMjxLZO+8fPK/+gO165xlk+QHz+3bA+6uC6UgSvTXdmEOwRG9Mr7pjg7/xtOYhqF8NBeUj24+fDKaO/ZM2B5uoHeOMyQ89bcE0OsJeP71t88nuke3H5CVL9MZkUyJ4vCDRET7fOBQLppboTQaW6I3JplQPo9L3trdtvveirDEDWKI3JptKpwMKnc0j20+4IJhaojcZ2JUbY7IpFA2mv7giSNbnfBWOunD4+4kUBxdiV90HG/8YtNn/2V1QfdToxmsmJKvRG5NNs5fBokuCLpc7XoWV3zqy/YjAGX8FVfOCrpYbn0S3PY/6/uHLmrwnqtkciCWzJUuW6OrVq7MdhjHj582H4d8/H8x/bd+IdqXNm6i/5kI6W8rw4ykiM2dSfuUVhGpqKDh5CZEZ00chYJNrROQ1VV2SaZ013RiTC465PEj05/7tiHeV2tVI+44CihbPJnba+bT8+Cc0ff/uvvXFF15A7Ze+RHTBghEfy0wMluiNyQUdTcG0ZMqId5VY/zYAVVctp+jjf07VZz7Drn/8NhKNookE+x59FK9lD3N+9csRH8tMDJbojckFfiqYupER78prbgx2NXVOMC0rY9q3/xGA92+5FYCyj310xMcxE4ddjDUmF2j6oukoPMXFawva+N3KmgMO4dPx1FMQChE79ljimzaR2LEDTaVGfEyT26xGb0wu6O0U8cavgkHO1AuSv+8PmPf65/sGVDt4sLWep1cBRTiV+zcD+V3pu2ZTKbZ94oq+5W5ZGRU3fIrqm29GBozHH9+6lb0//znRo4+m4sorUVVSDQ10/WkNPe+8Q2LLFgiFcIuLcSsrqfrcjYSqJuAQz5OAJXpjckFxLVTOg52vQ+ObIG4wjLI46Xmnf16cAX8yYNhjAVVatwR3yTpl+9fo3eIi5v3+dyR3NqLxHjQex+vspGPFSpp/+M8ktm2j9OKLKTjuOJzCQrZedjmaDAZL2/PTn5FqacFvbwdAIhEic4PnPnSsXw9AwUknUnrRReNzvsywWKI3JhfESuGv1ozKrqq6/4mWn9yH19lJKLJ/m390/nyi8+fvt6z8iivYfffdtPzff6XtP/9r/+0XLsQpLiZUV0tRRSWRefMoPPkkogsXIq4LQNvjj1P/5a8QmTFjVOI3o88SvTF5prem7be3Q0XFYbcXEWpvu42qz32O+ObN7P7hP+Pt3UvFdddRcfVVhy0fqguaiJK7dhE7+uiRBW/GhCV6YwwAbnk5hSefzOwH/nVY5cLTpgKQatw1BlGZ0WCJ3ph846V78DjumB0isX07O265Fb+zEz+ZAECi0TE7nhkZS/TG5Bn1PQDEGYXhj4FkYyMt9/0Lfnc36qUglSJZ30B840ZKLroIp7QEJ1ZA8VlnjsrxzOizRG9MvknX6Pc88CBOWSniuOA6A6bpB5UPUdeaP9H++BOE6uqQSAQJhZBQiKKzzmL63d9HIiO/ycuMLUv0xuSZyOxZSCTCngceGLV9umVlzP/DkzjWPDMhDTnRi4gLrAbqVfVSEfl/wKL06nKgVVUXZyi3DWgHPCA12OhqxpjRUXT66XzgzTdQVfC8YKhiz0O99E1XRzB0sRQUWJKfwIZTo/8isA4oBVDVq3tXiMjdwKHGVj1PVUf4CB1jzHCICIRCo/Ggwj77dnfTuLkVgJ6OVnxfqJ1byfSjakfxKGa0DSnRi8gM4BLgLuArB6wT4Crg/FGPzhiTUx76+5eIVrxHuHAX8X1PcnTsMvzH6oguPgGc4M7c1J4eChfXUrSkDrfE2u9zwVBr9PcAtwOZHlV/FrBLVTcOUlaBP4iIAj9R1fsybSQiNwE3AcyaNWuIYRljxpcy96Jv9r3q9u7lqRc+Cds2cGPP+Uj690Pbk9twoi7FZ0zLVqBmgMMOlScilwJNqvraIJtcC/zqELtYpqonARcDt4jI2Zk2UtX7VHWJqi6pqanJtIkxJss++q3FffO+77Crsf/hJVtDwZj66irxY6E+tZkNLz/PxldeZMuaV+np6BjvcE3aUGr0y4DLRGQ5EANKReQhVb1eRELAx4GTByusqg3paZOIPAosBZ4deejGmPG2udnnrke/xCVNT6KAV1xGJFaPHy1gVeNDvOqlhzzedHDZ487/EB/6wl+Na7wmcNhEr6p3AHcAiMi5wG2qen169YXAu6q6I1NZESkCHFVtT89/CPj6KMRtjMmC0+ZV8dfLpvDuo0FP/Atuvp1QrJAZFQVBE70fDJWs6SGUlWAc/N9+/5vEu7uzHP3kNdJ+9NdwQLONiEwD7lfV5UAd8Gh6jOsQ8EtVfWKExzTGZEkk5PDuo/3985/53t8RikQJRaPUzZ3PJ/726/uNad8rHI313bFrxt+wEr2qrgRWDnj96QzbNADL0/NbgBNGEqAxJnckE3GOPus81j23gvIpUympquH9t98klYjz3ptr6Olop6Ck9KByjuPge8Pvv29Gh90Za4wZss2rV7HuuRUAtDbupLVxZ9+6hacuI1JQmLFcV9s+xHHxPQ/HHbvB1kxmluiNMUM27agPUFZbx76mXcw76RROvPgyUvE40xYdTWFp2aDlSqtr2blpPb/+h9u59hvfS4+3Y8aLnW1jzJCV1tRy/mf+HICTL/koc44/kQWnnHbIJN+4eSM7N63ve+0fwRAMZmSsRm+MGZJkvIenfvpjGja+S7SwiGmLjhlSud3vbQXg2m98j2kL7QlU2WCJ3hiTUePmjTz/6wdJ9vTgpZKoKk1bNwNw1NIzCIXDQ9pPSWUVAF76QeNm/FnTjTEmoy1/epX33lxDKBKmoLSM7ra2vnUbX3mRrn2tQ9pPOH2B1kulxiJMMwRWozfGZOR7KRzX5cq//xYAqsrenQ289fSTAMRKMg19ZXKRJXpjTEZeKoXj9qcIEaFy2nTOuf6zR7ZD1VGKzAyXNd0YYzLyUynckNUF84ElemNMRkGNfuQ3N2UYEcGMM/u6NsZk9P47b9Hd3saGl58PFqQzdu+Y832PrjpouQycsHNjfx96kx2W6I0xGe2pfx+A//qnb4/K/iKFmYdHMGPPEr0xJqMv3PsATdu2UFpdQ99l1PQFVT3gwmrf60GWh6MxKqfPGMtwzSFYojfGZFRcWUVx+mYnM7HZxVhjjMlzluiNMSbPWaI3xpg8Z4neGGPynCV6Y4zJc5bojTEmz1miN8aYPGeJ3hhj8pwlemOMyXOW6I0xJs9ZojfGmDxnid4YY/KcJXpjjMlzluiNMSbPWaI3xpg8Z4neGGPynCV6Y4zJc0N+wpSIuMBqoF5VLxWR/wcsSq8uB1pVdXGGch8GfgC4wP2qOjoPoDTGTDjxrVvpeOop1PPB91DfB89HfQ98TS9T8DxU0+u8VPA65QXzKQ9NpYIdOg7iOMHUdUAccHuXueAI4rj927ku4giEw1RcfTXhKVOye0LGyXAeJfhFYB1QCqCqV/euEJG7gX0HFkh/OfwIuAjYAbwqIv+pqu+MJGhjzMTjtbXR+PWv0/XSywev7E3WvclYBNx0gg6FENeFkIu4A+ZF0l8YfvCF4Q/4wvC84Hm1nnfAej9Yl0jgFBRSfdPnx/9EZMGQEr2IzAAuAe4CvnLAOgGuAs7PUHQpsElVt6S3/TVwOWCJ3phJpPEb32TvL34BQOHppzHzJz/pT+Yiwfw40USCd48/AXxv3I6ZbUOt0d8D3A6UZFh3FrBLVTdmWDcdeH/A6x3AqZkOICI3ATcBzJo1a4hhGWNyXWJHfV+Sn/mTH1O0bBkSGk5jwhhRzXYE4+awF2NF5FKgSVVfG2STa4FfDVY8w7KMZ1dV71PVJaq6pKam5nBhGWMmgPjWrTR+/U4A5vzbbyg+55zsJ/n0rwedRIl+KGd8GXCZiCwHYkCpiDykqteLSAj4OHDyIGV3ADMHvJ4BNIwkYGNMbku1tJDc2ci+3/6WvT//OQCR2bOJLliQ5cjSepuJLNH3U9U7gDsARORc4DZVvT69+kLgXVXdMUjxV4GjRGQuUA9cA3xyhDEbY7LMa2sjsXUrifd3kGrcSWp3M8mGBrrfeotUY2PfdmWf+DgV11xDdOFCnGg0ixEP0JfosxvGeBrpb6hrOKDZRkSmEXSjXK6qKRG5FXiSoHvlz1T17REe0xiTBZpI0PSDH5DYuo2OlSvB9/vWSUEB4dpaCk86idjxxxGZMYPookVEZs4cfIfZYjX6Q1PVlcDKAa8/nWGbBmD5gNePAY8daYDGmNzw7vEn9M2XX301xeecQ3jGdCLTp+MUFWUxsmEaxx4+ucLujDXGDEndHV/tm2974gnafvc7Op5egdfensWohk8mYY3eEr0xZkgqb7iBBSuepu7v/o6Sc8+h6/U17L7nHjadex7db63NdnhHwBK9McYcJDx1KpX/43qmfec7HPX001R97kYAtl15Jd2vv57d4IZDxLpXGmPMUFTfcgteWzutDz/MtmuuZcHKFYOOH6OpFPHNm+lZu5bkzkbC06cTqq5GwmE03oPX2krnzhbeWOeyt90l4RRQHE1SUugRCyuhWIhwYZRYgUM46iLhEBIO44RcnGiUUGkRkfJSKhfUEi0pPHTgIpOq6cYSvTHmiDkFBUz9+p1EFy5k1ze/yaZzzyN2zDFU33orxeecjd/djde6j9aHH2bPQw+hXV2H3F9T9Qls+eBNlIVaKKGdjq4ou5PV+E74EKV8oDv91wy6CcdPEdIkMbooDCUpLvIpq4px1KUnU3X8/HSiH8UTkeMs0RtjRqziuk8SmT2brldW0fbY4+z4i784aJuCJSdTcfU1xD54LJHp00nU1+PtbUWTSSQSJlRRgVvvsPbnm7j4axdTNa0YCO5g9T0l0dlDvLmV7n09pLoT+MkUmkziJ5J48SSp9k6SbZ3sbUmS6EqS6EnR1ePQkYzS1F6K3xnhzR+u4/P/Ms9q9MYYM1wiQvFZZ1J81plU33or7X/8b+KbN+GWlOKWlRKePoPCU5YEI0+mRefOhbkH7Kd5V8Z9uyGhoKyQgrJCyo8gPlXlvpseIxkqZPNvnsH31BK9McYcKScapezSS0a0D8k4TNYI9ifCwikdvNMU5cmnIHTGtzlmzx5qVMd15MxssURvjMkdvZXsMci9533jak7b28HWx1azdlWcN1tm8N4/vMy8E2sory2ksDRCd0eC1l1ddOyN47jCtKPKWXTaVBxnYn8ZWKI3xuQMTWf6sapkF1QUc8x153L0J5V3X2rk7efqeePp9/FT/c04jisUlUdJJTzefamRorIos46tGpuAxoklemNM7hinZnMR4egzpnL0GVPxfaVjbw/dbUlixSFKKmM4rkPH3jgP3PECj9+3lmkLyokVh6idXcrx582YcM09luiNMTmjszUBQCrpH2bL0eM4QmlVAaVVBfstL66IcvY1C3nlv7ay/e0WADas2sWsYyqpmDKBxvbBEr0xJodsXRk8kO53d/+JqdUxVIMeM5ruJKME8731aRHBERBHcEISTB0HcQRxwJFgmUiQ0DPNy8DlIsHOBXa+V09DM8S7HBCltFKpngn1615m58bg+baO4zL7+MXEioqzds6GwhK9MSZnzE+k2Akk4h71DZ2IyICk3peDUfpbeQZ+CfiaXqf92wzcdngcIv5ekt2r8BKb6NmToGkTvLNi/61O+8S1LLvquiM6wnixRG+MyRlzLphF1TM7qP7ccYTrChFXoLfHi6T/IyCuE6zLQD0/uFk2XfvvzfzqK77noz74Xu+8oj6o76enwXbxjm6aH1hLlBLazzmf+X/2N+mdK77vo76HqvLQV79EovvQd/vmAkv0xpicIZHghqrm+9865HZOcZipX12KhA4el1FcJ3jMEQf30nQP2nowZYRvSnL25t00xxweeXYtc+bO4b2VT9MZbsVpaaKyso5kvIeNr7zIOf/jszjO0Pc+3izRG2NyRtHSKTiFIdzSCKnW+IC2l3Tji0LHC/V4bQka7nwJiblBDxgRSNfwSxM/IOa9gvY19KRp7y+D3uU+qId36t9DURXqRhE3Am4Y3AiFboRqL0UzEa6sqIDWfYQXRSnb/a9ISbCrs6afxJz6Zt57diVzz71gfE7SEbBEb4zJGW5JhOLTpx1ym4Ljq+l4vh5N+aBBcwsKQQO9UrDxORxtJV5yMaD9Dfu9LfXpLw23/U1C7CK06tZBj7V89md4t+jT3JDYzQORGpIFH8QLzyCUDB6T/VjsI4Rmh6m8814+e9oynFhsxOdgLFiiN8ZMKKGKGOUfmZ95pSrc2QpA9LgFA67U+uC4cNrNUDYj2NT3SW54Azr2gJdEvTiSSoCfgFQCvAROZxyAu849Hda/xQMtBbS3fZXQto6+Q6Yc+Oapn+EGJGcf8GGJ3hiTP1Lx/vlX7ifoY5luO4+3Qek0OP0WAMRxCH/gxEPuLvLso+DB715/hnffa8VJzsXZGxyjuHwTcWcHi2IL+dsLLiYci47JWxoNluiNMfkjHIOv7Tt4ebIH7qqDhjWw651gWbqt/v22FM1dPkURB0/B88FTSPnQ3tSBQ5wvPiVIqpwIe/t22dG6AGQW75a+yfELKsfn/R0hS/TGmPznpFPdW48EfwOc1fPLQxSsJEIzAHOjLVz/4SpOmvVBtu/dx/bWZh5Z3cr2xiU07vNYUDtGsY8CS/TGmPznhuDGP0JbPX23XPV2sn8o2OT/nJmgpaOHlvfXU7vvTc523qRA4rRoGcXSQ6l04zwZDM2wIL3bv6SLJ8Kn0NR+KgtqS7LwxobGEr0xZnKYuTTj4jllT/IRnmH66y9wUXItUUnRHYrxXtlSmsNlhHr2stMtYptbhIrbd/+WA5ze/AjHVQtT5ub26JaW6I0xk9o39IeclXie992ZvDvrWmpOupRpx53PB0KRwxe++3mmz1nUf/dujrJEb4yZvDp2c2ZqFfEFFzPzul8xc9jDD+d2gu+Vq90+jTFm7L1wD+IniZ7y6RE87ST3nz1rid4YM3k1vgV1H4RFHz6y8oVVsPVZ8L3RjWuUWaI3xkxe8TYomXrk5Rd9GFq3g5ccvZjGgCV6Y8zklewJbrI6UpK+69YdwoXbLLJEb4yZvFI9EBpBok/1BEneye1Uar1ujDGTmAbj4Ryp1u3gJeB3Xzlgtz4cfSksuHBk4Y2SISd6EXGB1UC9ql6aXvaXwK1ACvi9qt6eodw2oB3wgJSqLhmFuI0xZuT2bgv+aj4QDH4mLpRMgeOuOHhb34euluAu21h50EtnxhJ4+9/hnf+Ann3gp/q3j7dPvEQPfBFYB5QCiMh5wOXA8aoaF5FDjfRwnqo2H3mYxhgzyjoHpKSn7tx/3dp/h6JqaNkEu96GntaDy5fPCmr0EHwBDBQqgIvuPLhMlgwp0YvIDOAS4C6g9zfKzcC3VTUOoKpNYxKhMcaMhaJq+OsNEC0Jaue+BzvfgP/8S9j+UjAQWjgWJHknBOFCWPr5oNa+ZWWwrHJ+UKZsOsw+E6YthhmnQLggGBI5Rwy1Rn8PcDswcNSehcBZInIX0APcpqqvZiirwB9ERIGfqOp9mQ4gIjcBNwHMmjVriGEZY8wIlNTt/3rOMvirP2UnljF02KsQInIp0KSqrx2wKgRUAKcBfwM8LJLx1rJlqnoScDFwi4icnek4qnqfqi5R1SU1NTXDehPGGGMGN5Qa/TLgMhFZDsSAUhF5CNgB/LuqKvCKiPhANbB7YGFVbUhPm0TkUWAp8OwovgdjjDGHcNgavareoaozVHUOcA3wtKpeD/wHcD6AiCwEIsB+F1xFpEgkeF66iBQBHwLWjuYbMMYYc2gj6eX/M2CeiKwFfg3coKoqItNE5LH0NnXA8yLyBvAKQRfMJ0YWsjHGmOEY1g1TqroSWJmeTwDXZ9imAVient8CnDDSII0xBiCZTFJfX0/QYgyZLgtO5GVlZWVEo6P/kHG7M9YYM2G88MILrFy5MtthjJmZM2dy4403jvp+LdEbYyaMeDyO67pcf33QmNBbsx8o07LhbJut8i+99BJdXV0Zy46UJXpjzIQRj8dxHIe5c+dmO5RRt27dOrq7u8dk35bojTE5b+fOnaxYsYINGzYwZ86cbIczZgb7NTBSluiNMTklHo/zzDPPkEgkEBHa2tpYv349sViMCy64gNNPPz3bIY4JEbFEb4zJb11dXWzatInnnnuO3bt3E41GcRyHUCjE2Wefzemnn05BQUG2wxwzmQcWGB2W6I0xWbV7924ef/xxtm7diqpSWlrKpz71KebNm5ft0Mad1eiNMXkllUqxatUqVq5cSTgc5swzz2TRokVMmzYNJ8ef2DQWrOnGGJN3HnzwQbZv386iRYu45JJLKC0tzXZIWWWJ3hgzoTU2NvKb3/yGRCKBquL7Pp2dnRx11FFce+212Q4v71miN8aMuV27dtHc3MysWbOorq7GcRwcx+GUU07Jdmg5I5VKEQqNTUq2RG+MGXO9TRIf+9jHqKioyHI0uSmRSBCJRMZk35bojTFjrqGhAYBXX32V2tpaysrK8vLu1pHwfX/MLkJbojfGjLm2tjYAXnzxxb5l1113HaFQqK//+EinZWVlE7qfvV2MNcZMaNdccw2tra14nsfbb7/N008/zS9+8YtRPUZdXR0333zzqO5zPNkNU8aYCa+8vByAM888k7lz55JKpYD+9vuRTB977DE6OjrG4V2MLavRG2PyguM4zJw5c1T3uXnzZtavXz+q+xxvqjpmtfrJd/uZMSbvjGX79ngZy0RvNXpjTF7o7OzkmWee2W/ZgYlzKI/2G8syIsLs2bOpra09aL0lemOMOYSamhoAVqxYkeVIDi8UCnHddddRWVlJcXExjuP0/SKxRG+MMYM49dRT++6yPbAJZyiP+xuvMn/4wx944403eOCBB/Zb7rounuf1fWGNNkv0xpi8MBFGvLz88ss544wz6OzspKWlhe7ubjzPw/M8ksnkmD09yxK9McaME8dxqKurAxjX8fZz/yvQGGPMiFiiN8aYPGeJ3hhj8pwlemOMyXOW6I0xJs9ZojfGmDxnid4YY/KcJXpjjMlzkosjvonIbuC9AxZXA81ZCGcisHMzODs3g7Nzc2gT7fzMVtWMYyjkZKLPRERWq+qSbMeRi+zcDM7OzeDs3BxaPp0fa7oxxpg8Z4neGGPy3ERK9PdlO4AcZudmcHZuBmfn5tDy5vxMmDZ6Y4wxR2Yi1eiNMcYcAUv0xhiT53Iq0YvICSLykoi8JSL/JSKlB6yfJSIdInLbIOW/JiL1IvJ6+m/5+EQ+9kbh3FSKyB9FZGN6WjE+kY+9wc6NiCwd8Fl4Q0Q+Nkj5Sfe5Gca5mYyfm4tE5LX08tdE5PxByk+cz42q5swf8CpwTnr+s8A3Dlj/b8AjwG2DlP/aYOsm+t8onJvvAl9Nz38V+E6239NYnxugEAil56cCTb2vJ/vnZhjnZjJ+bk4EpqXnPwjUD1J+wnxucqpGDywCnk3P/xH4RO8KEfkosAV4e/zDygkjPTeXA71PJH4A+OioR5g9Gc+Nqnapaiq9PAZMxp4HIz03k/Fzs0ZVG9LL3wZiIhLNQnyjJtcS/VrgsvT8lcBMABEpAv4ncOcQ9nGriLwpIj/Lp5+ZjPzc1KnqToD0tHaM4syGjOcGQEROFZG3gbeAPx+Q3A40qT43MORzMyk/NwN8AlijqvFB9jEhPjfjnuhF5L9FZG2Gv8sJfj7dIiKvASVAIl3sTuCfVLXjMLu/F5gPLAZ2AnePzbsYG2N8bia0Izw3qOoqVT0WOAW4Q0RiGXY/GT83Qz03E9qRnpt02WOB7wBfGGT3E+dzk+22o0O0ny0EXknPPwdsS/+1AnuAWw9Tfg6wNtvvI1fODbAemJqenwqsz/b7GOtzk2HdCmCJfW6Gfm4m6+cGmAFsAJYNsXxOf25yqulGRGrTUwf4X8CPAVT1LFWdo6pzgHuAb6nq/8lQfuqAlx8j+GmWF0Z6boD/BG5Iz98A/HasYx4vg50bEZkrIqH0/GyCNtltGcpPus/NUM8Nk/NzUw78HrhDVV84RPkJ87nJqUQPXCsiG4B3gQbg/x6ugIjcLyK9I8x9N90l6k3gPODLYxfquBvpufk2cJGIbAQuSr/OF4OdmzOBN0TkdeBR4C9UtRnsc8PQz81k/NzcCiwA/n5A18neL4UJ+bmxIRCMMSbP5VqN3hhjzCizRG+MMXnOEr0xxuQ5S/TGGJPnLNEbY0yes0RvjDF5zhK9Mcbkuf8PrUK/WsfL3mIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  191282.0 people (VAP of 155360.0 ) and  115663.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a0bc82c4-52c8-4df7-a516-8183ef211631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#look for goofy tract that sticks into Canada\n",
    "for t in range(nTracts):\n",
    "    if tractGeom[t].centroid.y > 48.7:\n",
    "        if tractGeom[t].centroid.x > -95.5 :\n",
    "            plotPoly(tractGeom[t])\n",
    "            plt.text(tractGeom[t].centroid.x,tractGeom[t].centroid.y,t)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "30cc0163-44fb-44f6-bbc4-37c630c65fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#special for Minnesota -- trim tract 639 that sticks into Canada\n",
    "goofyTract = 2101\n",
    "t = goofyTract\n",
    "x,y = tractGeom[t].exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.text(tractGeom[t].centroid.x,tractGeom[t].centroid.y,t)\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-95.32,48.5)\n",
    "pt2 = Point(-94.8,48.5)\n",
    "pt3 = Point(-94.7,48.84)\n",
    "pt4 = Point(-95.32,49)\n",
    "trimPoly = Polygon([pt1,pt2,pt3,pt4])  #plan to clip off NE corner near Duluth\n",
    "xc,yc = trimPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2e9df779-4907-408b-8a9c-640b80ca65c5",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'MultiPolygon' object has no attribute 'exterior'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_103/2161636618.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgoofyTract\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgoofyTract\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrimPoly\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgoofyTract\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexterior\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcentroid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcentroid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'MultiPolygon' object has no attribute 'exterior'"
     ]
    }
   ],
   "source": [
    "#finish and display the trim\n",
    "tractMAP = tractMAP.difference(tractGeom[goofyTract])\n",
    "tractGeom[goofyTract] = tractGeom[goofyTract].intersection(trimPoly)\n",
    "t = goofyTract\n",
    "x,y = tractGeom[t].exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.text(tractGeom[t].centroid.x,tractGeom[t].centroid.y,t)\n",
    "tractMAP = tractMAP.union(tractGeom[goofyTract])\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b6f247ca-f873-4b52-bf62-0d43a5840297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fine, the clipped tract is in two pieces.  Pick one to add back into smooth map\n",
    "plotPoly(tractMAP)\n",
    "plt.show()\n",
    "plotPoly(tractGeom[goofyTract])\n",
    "counter = 0\n",
    "for geom in tractGeom[goofyTract].geoms:\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,counter)\n",
    "    if counter == 0:  #we'll add this piece back in to map\n",
    "        tractMAP = tractMAP.union(geom)\n",
    "        updatedGeom = geom\n",
    "    counter +=1\n",
    "plt.show()\n",
    "tractGeom[goofyTract] = updatedGeom  #slice off rest of tract\n",
    "plotPoly(tractGeom[goofyTract])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for SE corner\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-92.5,45)\n",
    "pt2 = Point(-92.7,43)\n",
    "pt3 = Point(-91,43)\n",
    "pt4 = Point(-91,45)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  220 tracts w pop 282600.0  to reassign to tracts...\n",
      "[202, 203, 204, 205, 790, 791, 795, 797, 803, 807, 992, 1545, 1546, 1701, 2709, 2714, 3003, 3007, 3010, 4263, 4295]\n",
      "I have flagged  244 precincts to reassign to precincts...\n",
      "[107, 133, 670, 814, 1222, 1225, 1289, 1290, 1468, 1949, 2047, 2138, 2199, 2483, 2614, 2661, 2690, 3159, 3167, 4055]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.15:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  282600.0 people (VAP of 219477.0 ) and  154411.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n",
      "working on tract 3200\n",
      "working on tract 3400\n",
      "working on tract 3600\n",
      "working on tract 3800\n",
      "working on tract 4000\n",
      "working on tract 4200\n",
      "working on tract 4400\n",
      "working on tract 4600\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tc2zGzpX0wV++OIdPP74GgEW/eIfSBxZaHJFSqjuPf85fEK1r9vDTFTv4b6B/x+WzhjFn9CAm56czJCMRhwhOh+ByCn9fe4D/e2Eb9c0eTeCx4txxOcQ5hRavvxrJGKNVKUrZRGqCyz+tYg/uYaUnxQGxN/n5gGqF0pHW5A1Q79YOPkrFouZA573EuNgaFGvAJ/BhmUnB5Rc/PGRhJEqpcDnR5J8b4Mz7XqXgzhfYUh4bTYgHfAIvazNZ6ryJ4Z9wQikVecvXHzxl3eONja75A7oOvL289ESrQ1BKhcGJRn8J/DsLJvHRKUMoCMzcZXeawNtoavHGXB2ZUso/nsrC6fksuWCs1aGE1ICvQmlr7oNvWB2CUirEPF4fB4818sKHh2Jubs0BXwLffd9ljLvrJQAqapstjkYpFWoVdSff14X3/BeXQ2hfA95Rj3SX00Giy0FCnJMxOSn85Utn43REVzPjAZ/Afe1eN20LrlRsGZKeyE+umk5pVf0p24VT3+dt3/bGQIvPR3OLjw0HjrOmpJqmFi8pUdYJKLqisUC8y8G8Cbm8ubMCgJrGFjKT4y2OSikVKiLC1UUj+nz+r1fuZsOB41FX+gatAwfgmjNPvrgZgR5bSikF4At8TXdE4TdzTeDA157+ILis1SdKqbZae2u7tAQenT7V5utVfbPHwkiUUtGmxevD5RAcmsCj07wJJ3tgllTWd3GkUmqg8foMLmf0JW/QBA7AhLzU4PKiX7xjYSRKqWjj9ZmorP8GTeAADMtK6v4gpdSA5DPReQMTNIEDkODS7vNKqY75jKEuSu+NaQIPeOTamVaHoJSKQlX1bqtD6FSPO/KIiBMoBsqMMYtE5G/AxMDuTOC4MWZmyCOMkIPHGrs/SCk14IzOTrY6hE71pifmrcA2IB3AGHNN6w4R+Rlg6xHS21Zx1TS2aIcepRRAsPmgz2eirilhj6pQRGQ4sBB4vIN9AnwKeDq0oUXWgy/vCC6nJw74EQaUUgHOQOnO28GAV1braR34w8AdQEczgs4FjhhjdnV0oogsEZFiESmuqKjoW5QRcPmsYcHlR1/bjbf9KFdKqQGptdQdjTmh2wQuIouAo8aYdZ0cch1dlL6NMcuMMUXGmKLc3Oidsuyha2YyNMM/I89Dr+5kxZbDbDt0IipfNKVU5LQOYuWLwhJ4T+oKzgMWi8gCIBFIF5GnjDHXi4gLuAKYHc4gI+XdOy/m839Yy8odFXzlz+uD20sfWGhhVEopK7nsXAI3xiw1xgw3xhQA1wKvG2OuD+yeD2w3xhzs9AFsRET4w+fP4msXj7M6FKVUlGjtxOPrqALZYv29W3ctNr952ZHfv1saXNbSt1IDW2sVSklVPYMa42nyeGl0e6l3e3CIkBTnJD8jkcEWTIreqwRujFkJrGyzfmNow7FefbMnantdKaUiLzne31P7k796t9NjRODCCbl885KJFA7PiFRoOiNPe1O/vyK4vPyWcyyMRCkVDT4+YyhpiS6aWnx4fIbEOAdJcU6SAom9qcXLhv3H+cv7+/nM46v5wvljuKxwCBPy0sIemybwLswakWV1CEopiyXGOfnYtPwuj7l4Uh4fnzGU2/62gYdf28m7uyv5+5fDXwDUsVDa+duSs4PLD7/WYdN2pZQ6zfi8NF74+lzOGJlFnCsyPTY1gbczZ0x2cHn2KC2BK6V6p6nFG7ERTjWBd2FKfrrVISilbMTrM3i8hgPVDRF5Pq0Db6fgzheCy7lpCRZGopSyix+/vJ3frNxzyrb7XtjKWzsryU1LYPrwDL7+kfEkxoW2ZK4JXCml+ik7Jf60bb99uwSA0qp63tldybwJuadU0YaCJvB2fnLVdL71zw8Bf2m85P4FSJROp6SUig5fnDuGm84bzfbDtRxrcJOZHEdmcjzZKfFsOHCca5etDstohloH3s5Vs4cHe14BjF76Ii3eKOxDq5SKKg6HMGVoOueNy2Hq0AyGZSaRGOcMdsUPx1hYmsDbERH2/GgBj3+2KLit8J4VXZyhlFKday0PhmMwLE3gnZg/JS+43NSiJXClVN+05u1wzGyvCbwLv7/xTKtDUErZXOs44o4wZFtN4F24aNLg4HLBnS9wvCF6Z6dWSkWnYALXEnjkfeXCscHlmT94xcJIlFJ21DqOuCZwC9zxsUmnrM/50auYKJxaSSkVnVpL4E6tQrFGyf0LgstHTjQzeumLuD16Y1Mp1b3WBB6O/iSawHtARCh9YCFbf3BpcNuEu1/i/57fSkllvYWRKaWindaBR4nkeBe77ruMswoGAfD4OyVc9NOVFNz5AvU6i49SqgMn68BD/9iawHspzung718+h1V3XswvrpsV3P7D57daGJVSKlppCTwKDc1M4uMzhgbX/7r2gIXRKKWilSbwKPbgVdODy1qNopRqL9gTU1uhRJ+ZIzKDy9oyRSnVXrAZoZbAo8+43NTg8vr9xyyMRCkVjVoHsdJmhFHI0ebW8hf+WGxhJEqpaGSCg1mF/rE1gYfA9xZNCS6HY8hIpZR9neyJqSXwqHTT+aODy5c98paFkSilok1roU5boUSxtXfNB2DnkTodK0UpBcDhmqZgb+1wzMzY4zkxRcQJFANlxphFgW1fA/4H8AAvGGPuCH2I9pCRFMeYnBSqdchZpQY8Ywy3PLWel7ccBiDe6SAtMS7kz9ObSY1vBbYB6QAichHwCWC6MaZZRAZ3dXKsu/vZTeytrOfexVN1EmSlBohmj5fl68rYXF6DMYbpwzMZNSiZ1XureHnLYW48t4BLpuQxPi+VjCSLEriIDAcWAvcB3wxsvgV4wBjTDGCMORry6Gxkx+FaxuSm8LlzC6wORSkVBh6vj4YWL2kJLkSEmoYWlvypmDUl1cHk/PT7J3tkTxqSxl0LJxMXjnFkA3paAn8YuANIa7NtAjBXRO4DmoDbjTFr258oIkuAJQAjR47sV7DRbMSgZIpLj2GM0RK4UjHE5zM8+vou/rCqlOMNLcS7HIzNTWXboRMAPHTNDD45cxjGwMFjjRw81kCzx8eZoweFNXlDDxK4iCwCjhpj1onIhe3OzQLOBs4E/i4iY0y7O3jGmGXAMoCioqKYvbt3zthsnv/wEMvXl3HV7OFWh6OU6oOqumY2HDhObZOHfVUN7Kmo48ODxymtamBCXiq3zBtLRW0zeyrqSI7P4tKpeVw+y/9+F4GR2cmMzE6OWLw9KYGfBywWkQVAIpAuIk8BB4FnAgn7fRHxATlARdiijWKzR2UBsD3wqayUsocGt4e/rNnPsxvK2Fx28v0rAsOzkhibm8qCwny+ctE4UhN6c9sw/LqNxhizFFgKECiB326MuV5EvgxcDKwUkQlAPFAZvlCj28ub/Xebw3GnWSkVemv2VvGbN/ewak9VcByjmy8YQ3pSHJdOHcKwzCSS4p0WR9m1/nycPAE8ISKbATfwufbVJwPJ0dpmAG46v8DaQJRS3Xr6/f0sfWYTQzMSuX7OKOZNzGXO6EEkxkV3wm6vVwncGLMSWBlYdgPXhz4k+/H6DFvKapg6NF1L4EpFsYraZpY+s4lXtx0B4L/fnBd11SK9oT0x+6GpxctzG8q45OdvsvFgDZfPGmZ1SEqpLry46RCvbjtCWqKL4rvn2zp5Q/+qUAa01XuruHbZagDG5qbwy0/PYmFhvsVRKaU6s/NILQ+8tB2AH185nZzUBIsj6j9N4L3U1OLlpc2HuHP5JgCW3TCb+ZPzThlWVikVHXw+w6ayGl7YdIin1+wnJcHJG7dfyJCMRKtDCwlN4N1we3ys23eMt3dV8H5JNR8erMHt9TFpSBq//WwRIwZFrs2nUqpn3t1dyZPvlVJceoyqejcOgQWF+dw2f3zMJG/QBN4hYwyby07wzAcH+fvaA9S7vbgcQuHwDD5/XgHnjcvh/HE5WupWKgoZY/jus5vZW1nPJ2cO5cKJg5k7PofsGKgyaU8TeECzx8u7uyt5bdtR3th+lPKaJpwOYWFhPgun53Pu2GxtYaKUDawtPcbeynp++Mlp3HD2KKvDCStN4PibFl3xm3c5UN1IcryT88flcNslE7hkch5ZKfFWh6eU6oX7X9oGwOIZQy2OJPw0gQO3/vUDDlQ38tj1Z3DRpMEkuOzVmF8pdVJdk4dR2clhGb412gz4duDGGDyBKY9++3YJ2w/VWhyRUqo/slPj2VfVwN6KOipqm2lwe/zvc6+Pyrpm6ps9VocYMhLJ3u9FRUWmuDj6Zm73+gz/XHeAn6zYQWWdmyvOGMa3PzaJvPTYuVut1EDx/ec288f39p2yTeTk7PAJLgcLCvP5VNEIzh4zyBbDP4vIOmNM0WnbNYGfVNvUwq/e2MMT75TgdAi3zh/PkrljtLWJUjbS2vZ7T0Ud9W4vDc0e6t1eHAJZyfHsOlrLcxvKqW3y8NWLxvKtSydZHXK3NIH3wv6qBn74wlZe2XqExTOG8tA1M3FqElcqZtQ1e5j2/RUAfHfRFG46ryCqS+KdJfABXwfekZHZySy7YTbfunQi/95YzkOv7LQ6JKVUCKUmuHjx63OZNyGXHz6/let/t4YXPjxEbVOL1aH1ipbAu/HNv2/gmfVlzJ+cxw8+MZWhmUlWh6SUChFjDL9/t5TH3tzD0dpmMpLieP5r50ddD2utQumjFq+PJ94p4aFXd+IQ4ewx2WQlxzMoJY7M5HiykuOZnJ/G5Px0240lrJTy8/oMv3tnLz96cTs/vrKQa86Mrvl7O0vg2g68G3FOBzfPG8uCwnx+9t8d7Dpax47DtVTXu2ls8QaPczqE8YNTmTo0g2nD0pk5IpMZwzP1BqhSNvDipkP8dIW/qnSwjVqfaQLvoRGDknn42lmnbGtq8VJR28yW8hNsKa9hU1kNb+48yvL1BwEYnJbAZ+aM4qsXjcUV5tmplVK9t6W8hjuXb2JTWQ2FwzK47qyRXDgh1+qwekwTeD8kxjkZMSiZEYOS+di0IYC/Tu1obTOr91bx3IZyHnp1J5vLa/jlp2dpD0+loogxhgde2s6mshq+dvE4bv3IeNsVtOwVrQ2ICHnpiXxi5jCeuPFMvrdoCq9sPcJ1y1YHJ05VSlmnprGFp1bvY9Ev3uHtXZWMH5zKzfPs+S1ZS+BhdtP5o6lr9vDzV3ay43AthcMzrA5JqQGrrtnDBQ++QU1jCxPz0rj/ikKuOGOYbb8dawKPgDNGZgGcctNTKRV5iS4HNY3+tt4v3zY3qjvv9IT9vjPYUFK8/8/cpAlcKUs5Agl7xohM2ydv0AQeEfFO/9ezZq0DV8pSa0qqAYiLkea9msCVUgNGVX0z4K8Lj2QnxnDRBB5BsfGZr5R9LSzMZ96EXLYfruXIiWarw+k3TeARZP/Pe6XszeMzjMr2j3Py5Hul1gYTAtoKJQJa75XEwlc2peyo2ePl4Vd38Y/iA1TWuQGYFWgdZmc9TuAi4gSKgTJjzCIRuQf4ElAROOQ7xpgXQx+i/bXe+fZp/lbKEg++vIPfvVPC+MGpPHDFdOZNzCXOhh132uvNFdwKbGu37SFjzMzAjybvTjgCf2WflsCVssTZY7IB2F1Rxz/XHaShOTaa9PYogYvIcGAh8Hh4w4lNrSVwzd9KWeOSKXkU3z2f88fl8PKWw+ytrLM6pJDoaQn8YeAOoH1D5v8RkQ9F5AkR6bBCSUSWiEixiBRXVFR0dMiAoR15lLJOTmoCWcnxAAzLio2JWbpN4CKyCDhqjFnXbtdvgLHATOAQ8LOOzjfGLDPGFBljinJz7TNMYyi1DmLV4tWOPEpZaUHhEFwO4UtPtk9n9tSTm5jnAYtFZAGQCKSLyFPGmOtbDxCR3wLPhylG22u9WZKeFGdxJEoNbOeMyWHikDRKKuowxti+O323JXBjzFJjzHBjTAFwLfC6MeZ6Eclvc9jlwOYwxRgztA5cKWtd9dgqtpSf4CsXjbN98ob+tQN/UERm4u+fUgrcHIqAYlEM/J8oFRM8gba8lwUmYLG7XiVwY8xKYGVg+YYwxBPTjPbFVMpSZxZkUVJZT05qgtWhhIT9W7LbQGsBXKtQlLLOqt2VLF9fxswRmSTH23MCh/Y0gSulYt6KLYf54pPFZKfE8+QXzoqJ+m/QBB4RwbFQrA1DqQHrrn9tpsHt5eZ5Y4lzxE7ai50riWqx8WmvlF0tmp5PaoKLHz6/len3ruBTj73HI6/uovx4o9Wh9YuORhhBOhqhUta4Z/FUli6YxOq91azaXcl7e6t45LWdPPr6Li6bNoSbzh8dnLvWTjSBR0CMVLcpZWsJLifzJuQyb4K/R/jBYw08+d4+nn5/P89/eIgZIzK5evZwLpyYy/CsZIuj7RlN4EqpAWl4VjLfWTCZWz8ynuXrD/LHVaXc/ay/P+Ki6fn84rpZUX+zUxN4BET3v4BSA1tKgovPnlPADWePYk9FHUv+tI7nPzzEvqoGpg1LZ1R2CtcUjSArJd7qUE+jNzEjSKvAlYpeIsK4wWn86tNncPmsYaQkOFm+vowHXtrOgkff5kB1g9UhnkZL4BHQmrej/NuYUgqYnJ/OQ9fMBPwND/68Zj93P7uZuQ++QekDC60Nrh0tgSulVCdEhG2HTgTXo60lmSbwCGgteOuUakrZS4Pbw5/X7AfgjdsvjLqbmprAI6B1MmNnDPUAU2ogSI4/Wcv89q7om1FMM0pEaMlbKbu6/4pCAAanJVocyek0gUdAa81JdH35Ukr1xFmjBwHQ7Im+OW01gUeAtkJRyr6i+daVJvAIOFkC1wyulF1F2w1M0AQeEa0z8UTh66+U6lb0FsE1gSulVBei+R6WJvAIiOZ/AKWUfWkCj4BgAtcMrpTtRHMjBE3gEXByNvoo/A9QStmWDmYVAVoCV8peDtU0cs79rwMwKts/uUM0tiLTBK6UUu0kxTmDyxPy0jhjZBZnFkTflGtahRJB0ff5rZTqSFOLL7j8ytYjVNQ2Mzhdu9IPSCerUDSFK2UHmclxLLlgDJ8qGg7A9sMnujnDGlqFEgHBjjwWx6GU6pnEOCffWTAZ8FenPLuh3OKIOtbjEriIOEXkAxF5vt3220XEiEhO6MOLDXoTUyn72nCwhprGFqvD6FBvqlBuBba13SAiI4BLgP2hDCpWaQJXyl6e/aCMjQeOn3JTM5r0KIGLyHBgIfB4u10PAXcQzYMFRAH94yhlT0+8WwJAY0v0DSULPS+BP4w/UQdvzYrIYqDMGLOxqxNFZImIFItIcUVF9M1oEQmt8+hFYztSpVTnfnLVDEQgLz3B6lA61G0CF5FFwFFjzLo225KBu4DvdXe+MWaZMabIGFOUm5vbr2DtKlgC1/ytlK1MHJLGFbOG44rS6RB70grlPGCxiCwAEoF04E/AaGBjoGnccGC9iJxljDkcrmDtSgezUsq+TBRXgnabwI0xS4GlACJyIXC7MebKtseISClQZIypDH2IsUPbgStlQyZ6GyBE5/eCmBO9n+BKqa4Z4OCxRg5UN1gdyml61ZHHGLMSWNnB9oLQhBObtApFqej1x1WlPPFuCcKpRa3W9+3+QOL+0pPFvHzbBRGPryvaEzMConk8YaUGund3V7KvqoFPzBwKnFrQEhFmj8piS3kNv/7MGdYE2AVN4BGgkxorFd0mDUnjkWtnWR1Gr2kdeARpCVwpFUqawCOgtSOPUkqFkibwCNAJ1ZRS4aAJPAKMZnClVBhoAo8gvYmplAolTeBKqZh35ERT1I7p3R/ajFApFdO8PsNHfvYmdc0epuSnM2fMICYPSWfs4BQKh2VaHV6/aAKPgNbmg9E8KI5SscrrM9Q1e4h3OshMjuPp9/efMmkx+NuB25EmcKVUTGstON06fzxfvWgcHq+PsuONrN5bRUVtM3XNXs4YmWltkH2kCTwCHIEi+Nef3kCCS287WMXr6/s3oL52wurPbetIj17Z52vs89+mbyf29vncnlNL2y6ng1HZKYzKTunT80cTTeARMHVoOp87ZxR1zdE5LdNA4XT0LWn0teqrP/23+npqX5+zz9V7kT2tz53iHA7h0ql5fXzW6KUJPAJSElzc+4lpVoehlIox+n1eKaVsShO4UkrZlCZwpZSyKU3gSillU5rAlVLKpjSBK6WUTWkCV0opm9IErpRSNiWRnO5LRCqAfRF7wpNygEoLntdKA/GaYWBe90C8ZhhY1z3KGJPbfmNEE7hVRKTYGFNkdRyRNBCvGQbmdQ/Ea4aBe91taRWKUkrZlCZwpZSyqYGSwJdZHYAFBuI1w8C87oF4zTBwrztoQNSBK6VULBooJXCllIo5msCVUsqmYjaBi8gMEXlPRDaJyH9EJD2w/TMisqHNj09EZlocbsh0dt2BfdMD+7YE9idaGWuodPFaF4hIY5vX+jGrYw2lrl7rwP6RIlInIrdbFWOodfFan9Xmdd4oIpdbHWtEGGNi8gdYC8wLLN8E/LCDYwqBvVbHGonrxj/70ofAjMB6NuC0Ot4wX3MBsNnq+CJ93W32Lwf+AdxudawReK2TAVdgOR842roeyz8xWwIHJgJvBZZfAa7s4JjrgKcjFlFkdHbdHwU+NMZsBDDGVBljYmWSzp681rGo0+sWkU8Ce4EtkQ8rrDq8ZmNMgzHGE9ieSN+n3bSVWE7gm4HFgeWrgREdHHMNsZfAO7vuCYARkRUisl5E7rAkuvDo6rUeLSIfiMibIjI38qGFVYfXLSIpwLeBey2KK5w6fa1FZI6IbAE2AV9uk9Bjlq0nNRaRV4EhHey6C//Xq0dF5HvAvwF3u3PnAA3GmM1hDzTE+njdLuB84EygAXhNRNYZY16LQMj91sdrPgSMNMZUichs4FkRmWqMORGRoEOgj9d9L/CQMaZORCITaAj19X1tjFkDTBWRycAfReQlY0xTJGK2iq0TuDFmfjeHfBRARCYAC9vtuxablr77eN0HgTeNMZWBfS8CZwC2SOB9uWZjTDPQHFheJyJ78H8TKQ5jqCHVx9d6DnCViDwIZAI+EWkyxvwybIGGUD/f1xhjtolIPTANG73WfRGzVSgiMjjw2wHcDTzWZp8D/9evv1oTXfh0cd0rgOkikiwiLmAesNWaKEOrs2sWkVwRcQaWxwDj8dcLx4TOrtsYM9cYU2CMKQAeBn5kl+TdnS5e69GB/2tEZBT+uvJSi8KMmJhN4MB1IrIT2A6UA79vs+8C4KAxJmbezG10eN3GmGPAz/Hfxd8ArDfGvGBVkCHW2Wt9AfChiGwE/om/XrTaohjDoav/8VjV2TWfD2wUkQ3Av4CvtH7bjGXalV4ppWwqlkvgSikV0zSBK6WUTWkCV0opm9IErpRSNqUJXCmlbEoTuFJK2ZQmcKWUsqn/D80nc6OiAciTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.7868852459016393 122\n",
      "100 0.7411764705882353 170\n",
      "200 0.6949152542372882 177\n",
      "300 0.7324561403508771 228\n",
      "400 0.0 0\n",
      "500 0.7527386541471048 1278\n",
      "600 0.6351209253417456 951\n",
      "700 0.6090594345066359 3466\n",
      "800 0.6122448979591837 49\n",
      "900 0.7423728813559322 2065\n",
      "1000 0.8421052631578947 57\n",
      "1100 0.8958333333333334 96\n",
      "1200 0.7142857142857143 112\n",
      "1300 0.4627027027027027 2775\n",
      "1400 0.7962962962962963 54\n",
      "1500 0.65234375 256\n",
      "1600 0.8786610878661087 239\n",
      "1700 0.75 620\n",
      "1800 0.9081632653061225 392\n",
      "1900 0.6695652173913044 115\n",
      "2000 0.5 30\n",
      "2100 0.6511627906976745 43\n",
      "2200 0.6519083969465649 2620\n",
      "2300 0.8080808080808081 99\n",
      "2400 0.732824427480916 131\n",
      "2500 0.6774193548387096 279\n",
      "2600 0.7200282087447109 1418\n",
      "2700 0.3444794952681388 1585\n",
      "2800 0.3269330565646082 1927\n",
      "2900 0.5006321112515802 791\n",
      "3000 0.761485826001955 1023\n",
      "3100 0.0 0\n",
      "3200 0.5285565939771547 963\n",
      "3300 0.41008403361344536 1190\n",
      "3400 0.19899244332493704 1588\n",
      "3500 0.1617391304347826 2300\n",
      "3600 0.07174490699734277 2258\n",
      "3700 0.1702127659574468 235\n",
      "3800 0.3938294010889292 1653\n",
      "3900 0.4829244829244829 2079\n",
      "4000 0.0 0\n",
      "4100 0.6861538461538461 650\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 26.327870508507043 21.398317136173244\n"
     ]
    },
    {
     "data": {
      "image/png": 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5f8OG0KZN6ZuLr/C51QjhzF15hk5I5ssZg2v8nEI4o8WUFnRt3JWvbvnK6igu9cMPpnVcnkaNzMzQNWsgMxPq1TOt+NOnzeN27DD96BkZsHev6ccuLDRvCg8+COedV/U8NpvpQnnzzTPPK2Uu6PbpA3PnnnubM+UsNta8UdStawr8nj0mf7GkJPjf/8zFVG9R4+VklVLBQAqwX2s9VCnVDXgTiAAKgXu01isqew5XFXCAafPWcv/IbgCs3HyQpPbSUSbcr/4/6tOkdhM23bvJ6ihuc/iwKcY7dsA115hzYWFmL9BiiYmmlZuZWXouOtr0V198MTz+uLk4WRN2O3z5pTlu2NA8X/Pm5i8GMG8gK1eabpZmzcz+pZmZppUdGWmGW7ZubS7g1q5tRvTUrl1+H7/NBps2mTH2zz1nJkqlpTn3V4MnuGI52UnAZqD4W3oFeF5r/Y1S6sqirwfXNKjTYUZ0A0wRH/rnvRxaJQVcuF+Xxl3IzM10fEcf1rix+QgLM63V8HDTpTJokPn6b38zXRoXX2xWT+zTB+rXd/1SuEFBpW8g5YmMPHekSkyM+QB4+OFz71+R4GAz5r5LF/N9P/GE6Tq65RYzA7Z4MlVqKrz/vumuSUkxP4fjx01XT+3a5i+AjRtN339x6/6PP0wL//bbXb9fqlMtcKVUHPBv4EXggaIW+GLgHa31R0qpkcDVWutbKnseV7bAS7OZz7I7j/CEmxfczMK0hWQ/4V/94KJUfr6Zzfr223DqlDk3erRZlmDbttL7xcebLiNnpaZCx47Vy1TTFvhU4BGKB2Ab9wOLlVL/xEwIurCCFx4LjAWId0OnUnjTbeQdbEP68Rwp4MLtsvKyyCnI4fjp49SLrGd1HOEGYWEwZQq8+qrZ2/Tpp02/fliYGYM/Z44ZN19s927T55+eblrdeXmwcKEp8GvWwDvvmL8Gqlu8K6W1rvQDGAr8q+h4MLCw6Hg6cH3R8U3A946eq2fPntrVrrr3Jw1ahzdN0+u2H3b58wtR1uRfJ2ueQx88edDqKMLLbdigdVSU1gMHal1QULPnAlJ0OTXVman0/YBhSqndwDxgiFLqfeA2oHhZnY+B3i56T6mSz6YNpFG3FeQdbMP5rRtZEUEEkMiQAB50LJyWlQXXX28mKc2bV3rh1dUcFnCt9eNa6zitdQIwAvhRaz0KOAAMKrrbEGBbBU/hViHBQUx/tXRd8PTj0jcp3Kd4Or1C5h6I8mkNd9xhRvHMn+/emaQ1eV+4C5imlAoBcinq57bC8EGtS47j2qWTn55oVRQRIJSSAi7KN22aWRL4lVfcv55LlVYj1Fona62HFh3/orXuqbU+X2vdR2u9yj0RHQsLDeZ0XiEABUcSmTpvrVVRhJ/THpz4JnzPsmXmguW118JDD7n/9XxuOdmKlF0j5f9GdmN12iEL0wh/J10o4mzp6Wa8fEKCmUTkiT/S/KaAw5nTaHu2a0JufqF1YYRfKu4DF6Ism82s5HjsmOk+qeuh7Xr9qoDDmUV83Au/WRdE+LWvtvnXWiiiZp591qzkOHPmmWPE3c3vCjjAn+5KBuA/fxvAiVO51oYRfqVbk24A3P757bSc2pJ1h9ZZG0hY7ptvzM5Cd9xhZmx6kl8W8EVvDiIibisA9RpavG+U8Cv94/vz7ahviQyJZG/mXm5acJPVkYSF9u6FUaNMq3vGDM+/vk8uJ+uMskvO5uXbCAsN9sjrisCgtSbor6b9c0f3OxicMJjXV7xOz6Y9+X7X9zSr04x8Wz71I+vz8Y0fl+zmI/xHfr4ZJrhpk1mfvE0b972WK1Yj9CllN3kIDwvGZpONH4TrKKV4a+hbfL71c+asmcOcNXMASD2SSrM6zcjKy2L1wdUA7Di2g06NOlkZV7jBww/D8uXmoqU7i3dl/LaAAxw+lk3j+rUA+HzpdoYPsuinLPzS2J5juavHXXyz/Rs+2/IZt3S5hQHxAwgOMn/tfbr5U66bfx35tnwHzyR8zccfm52A7r/fTJm3il8X8IZ1o0qOf1mdIQVcuJxSiivbXMmVba4857bivTOjQqPOuU34rrQ0c8Gyb1/4xz+szeKXFzGLZeWUXsBcskwuZgrPKt47c37qfPZmVmHhaOG1cnLghhvM0rLz57t2w+nq8OsCHlO79MLRjk0eGlkvRJGMnAwAnkl+hhvm32BxGuEK995rdtz54ANo0cLqNH5ewAtt9pLj1J9bV3JPIVxvc8bmkuO518+t5J7CF7zzjpki/9RTcNllVqcx/LoPvKzoWi7ejE4IByKCI2hcqzGHHpJ1eXzdunWm9X3xxWbWpbfw6xb4gYyTJcd1oizurBIBZ336eg5nHy7pShG+KTPT9HvXrw9z55pt07yFX7fA4xtLv7ewzrHTxwBImJpATEQMQSoIpRQKdc7xuJ7jePDCBy1OLM5WvDnDrl2QnAyNvGzTL78u4AARzbeSu7+d1TFEAJpy2RQ+3/I5mqI9DNHYtb3k6+LjRdsX8f2u76WAe6Hp0+F//zMbHPfvb3Wac/l9AV/4WQSX9LI6hQhEQ9sOZWjboQ7vlzQriSDl172ZNZaebi4gfvKJmcIeFgahoaWfyx5X5bbiz+HhEBFhPhd/HD5sNmW45hp40EvfW/2+gHdq1cDqCEJUyqZtBCsv6lj1EnY7/PADzJ4Nn30GBQXQpw/ExZkiXlBgPmdnl35dfK6iz1WVmOi5zRmqw+8L+Bc/7wK6WB1DiArZ7LaS6fcCDh40RXP2bNP33KABTJwId94JHTpU/3m1NhsvnF3Y8/LMcW6uOc7LKz3u2xdiYlz1nbme3xfwG4a0ZpzVIYSohGyQbArrt9+aov3FF+briy4y62wPH266N2pKKQgJMR/+wo++lfIVFJZO5kles5fB3eMtTCPEuRQKu7Y7vqMf2r/fTJB5+22ztnZsLDzwgGltt21rdTrv5/cFvH50ZMnxDbceI2OjFHDhXYJUUEDtdl9YCIsWwaxZ8NVXpq/7kkvgn/80FwytXl/El/h9AQ8NKb26P/LWHAuTCFG+IBUUEC3wvXthzhzzsX8/NG4Mjz5qxlmfd57V6XyT3xfwsv79dm1mPGp1CiHO5M8FvKDAtLJnzzZ7R4JZR2TGDBg61AzhE9XndAFXSgUDKcB+rfVQpdRHQPEMmRjghNa6m8sTukBwzH5sJ5pzcntXq6MIcQ5/LOC7dpmW9jvvmFElzZqZRaDGjIGEBKvT+Y+qtMAnAZuBaACt9c3FNyilJgOZro3mOrYTza2OIESF/KWAFxSYESSzZsF335lRH1deCXfdZT770+gPb+HUj1QpFQdcBbwIPHDWbQq4CRji8nRCBABfL+Dbt5tRJO++a2ZMxsWZFfvGjPGONbP9mbPviVOBR4A65dw2ADistd5W3gOVUmOBsQDx8daPALHbZXNj4V18sYDn5ZnZkbNmwY8/mhX6hg6FsWNNH7c3rdjnzxwuwKCUGgqka61XVXCXkcCHFT1eaz1La52ktU6KjY2tZsya+SS59L2l74glpB/PtiSHEOUJUkHYtM3qGE7ZutWsDxIXByNGwI4d8Le/mREmn31mukqkeHuOMyvo9AOGKaV2A/OAIUqp9wGUUiHAdcBHbkvoAsMHtaH/qGQAVn48uGSneiG8QWRoJKcLTlsdo0K5uWYLscGDoX17mDYNBg2CxYth50548klzkVJ4nsMCrrV+XGsdp7VOAEYAP2qtRxXdfAmwRWu9z40ZXWLpfwdbHUGIckWHR3My/6TjO3rYypVmDZLmzWHUKNi3D156Cf74AxYsgEsvhSBZRNFSNb0uPIJKuk+81dAJycBgi1MIYUSHRXMk+whfb/uarLwsMnMzqR1Wm97Ne9OmQRuPZtm82ey2Pn8+bNpkzt10k+nbvugiKdjepkoFXGudDCSX+Xq0a+O4z6nT+YCZozthVEtrwwhRRrM6zTh6+ihXzb3qjPMhQSG8dPFLPHThQ259/S1bTMH++GOz47pSMHCgaW2PGmX6u4V3Up5cgyEpKUmnpKR47PXKatJzOYdX9wHAZpORKMJ75NvyWXdoHTZto254XWqH1eZk/kke/f5RFqYt5JYutzBr6Cxqhbnu2s3WraVFe8MGU7T79zet7euvh6ZNXfZSwgWUUqu01klnnw+YofUNm+RxuOjYrjVBSAEX3iEsOIxezc/dNuqD6z5gyL+HMHfDXO7vc3+596mKtDRTsOfPh/Xrzbn+/c22YddfLxcifVHA9Gj9Or9PybHdHjgrvwnfFR0ezZMDngQgNLjiRUO2bDGr+dWvD/HxMG+e2bwAYNs2+PvfoVs3aNfOTGevUwemTjUXJZcuNRcqpXj7poBpgUfXCi85DguVgarCNxTYCwAIDTq3gL/+uim+xe65B5Yvh5Ej4Y03zPC/4h7LCy6AKVPghhukT9ufBEwBT911BLBmIpEQVVVgK+DNlDeZvmI6ABk5GXy55SsaF/amd+dYtD6zeH/6KVx7rdnJ5q23YPJkqFvXrLF9000ypd1fBcxFzOCYg9gzzZWZAFo7X/io1PRUOs/sfObJlePhq5mEh2suuECRnGxOf/ed6UIR/quii5gB0wf+2Et7S46zsvMsTCKEY50adWLJ6CXc3u12xvYYy+RLJ9Ot7kUAtIi3k5kJSUlmH0kp3oErYFrgGZk5xMZElXwtQwmFr7nmviV8MWMQuw+doGXjGKvjCA8K+BZ4w7pR7Nx/ouTrhP6/WRdGiGoo2bxeS8NDGAFTwAESm8WUHP/x24XWBRGiGpTUbXGWgCrgABNe/tXqCEJUSyDtXC+cE3AF/NX/611yfPuzSy1MIkTVFBYtGR4aEnD/bUUFAu43ISKsdOj7e38dYGESIaqmsNB8Lvs7LAJbwBVwgI9/TCs5jmm/1rogQlSBragFLjOJRbGALOA3XNSWzlctASBzazeuf2CJxYmEcKykgIdIARdGQBZwgA0LBzH5gzUAfDJlENFt1rPvSJbFqYSoWHEBDwkO2P+24iwB/ZvwwC3defrNFQCc3N6VFo2iKbT51u7gInDYbICyyQQ0USKgCzjAX8f1xmYrHZ61Y/9xC9MIUTGbDQjyjd3rhWcEfAEHzmjRfLhop4VJhKhYcQtciGJSwIs07LoSgBkz5EcivJNdWuDiLFKtirTumA3AsY09LU4iRPlsNiUtcHEGKeBF/nJDfasjCFEpmw1UkFxkF6WkgBe5tK9sWSK8m92upAtFnEEKeJHzmtej1eBfALh6YrK1YYQoh80GSkkLXJSSAl7GRzPPA+Db+fEWJxHiTIU2O39sbQDShSLKcLqAK6WClVJrlFILy5ybqJTaqpRKVUq94p6IntM6rh4q4gQtu+y3OooQJfYezqRW811k7+5EaK2TVscRXqQqy5pNAjYD0QBKqYuAa4CuWus8pVQjN+TzqNFP/o7OHcy9d9axOooIYCdO5TJ84u9sTY2gY9dcdmwLJf9wP65/YAnPT+hodTzhRZwq4EqpOOAq4EXggaLTdwMva63zALTW6W5J6EG7dysIO8WkEd2sjiICzMadR/gp5QDXD2nFJbeuYfOiwYQ02MsPK013Xu3zNrBg8iCLUwpv42wXylTgEaBsB1xbYIBSarlSaolSqld5D1RKjVVKpSilUo4cOVKztG4W10JDfm2ZTi88xm7XPDL9d7p0iOC+m8+neWwdNi8aSGSLLRRkxLP7YCbzvtvKrlXnWR1VeCGHBVwpNRRI11qvOuumEKAe0Bd4GJiv1Lm79mmtZ2mtk7TWSbGxsa7I7DZXX9wAgBfe2mhxEuHPftu4n57XJRN3wW+E1D7Bq5P6QpCdh6f9zpAxyQz8SzJpq5oB0LJJXW6+pB0N60ZZnFp4I2e6UPoBw5RSVwIRQLRS6n1gH/CJNhv1rVBmfFNDwLub2ZWw2c2iVlvSZKytcL2Vmw9y/9+28uu8/mBvDhGZtL5wPb16a24fHs+feve1OqLwMQ4LuNb6ceBxAKXUYOAhrfUopdR4YAiQrJRqC4QBGe6L6n7pR/MA6Ncn3OIkwt+89N4qnhjbHgoGE9/vV155rgE3X9IOkG39RPXVZHO9d4B3lFIbgXzgNu3j22Zv2pYDwMgrEqwNIvzK17/t5Ml7WhHRaB8LP4vg4qQLrY4k/ITyZM1NSkrSKSkpHnu9qtix/zhd+x0k91gDCk40kkXzhUvY7ZqQOsfROfX56Put3HRxO6sjCR+klFqltU46+7zMxARem7uGtu0LyNnXmrse3SbFW7jM7M83onPqU6/jainewuVq0oXiF35I2cODY84jNPoo/1lwlD9f1t/qSMJPLF6+i/snhKMij/Plhz4/z014oYBtgf+yfh89r0vmkn71UUE2fvw+lD9f1sHqWMIP/O2dFBp3X8HlF8STe6wxj72aRr+ucVbHEn4ooFrgdrtm9ucbeeGVU+xf3htUE1r2W85brzajf9dEq+MJP7B+RzpP35EEodlcOHIpbzzXgW5t+lgdS/ipgCjgufmFPDJtBe/MjCZ7VxdU5HH63LSUGc+2o1eHflbHE35k9oLtQCNmL9jFncMGWx1H+Dm/L+A5uQU06riV7F0XEtpoFzc/soTpjyXRqN5gq6MJP/T1Ijsq8ji3XCoXLIX7+X0Bn/TKcrJ39eeyccksfGMgIcHSVSLcJ31vXWISdxEV0cPqKCIA+P1FzEaxoQAsWdiUlZsPWpxG+LvQiAKOb2/D5A/W8EPKHt5dmMqL76aQk1tgdTThh/y+gL94dx/+9k4KuRlN6H9BKG9/IQtVCffpeWEW5NfhoVHduaRXS8Zc3YmnxiRRL3EvL7139npwQtRMwMzEXLhsB8OvCaEwszEz5m5lwo3nW5JD+L/Fy3exeVcm67ecJLZBKLl5dqY/bKbPHzx6iib1a1ucUPiaimZiBkwBB9i69yide5xCBWkObW9M/ehIy7KIwDL+xV9466n+RLdZR+qyROJio62OJHyITKUH2sU34B/Tj1FwJIGBI1dit/v02lvCh7z5ZH/ueWkZWTs60qb7QZ55a4XVkYQfCKgCDvDALd3peX0yqV8PpP2lv/BDyh4OHTtldSwRAN54rB9Pvb6GvMz6vHBPDzbv9unVl4UXCLgCDrBi/iAG3JrMth8GcEmvljRtUJug2hk07rGC/5v8K3sOZVodUfip58f1ovPgTWAP4fDxHKvjCB8XUH3gZ/v3V5tYtuYYf+wvZOf2YLavaI09qykAkfGbuf3uY7zxmMzUFK5xIOMkA25cx87k/tRtt5aM1K6EBAdkG0pUUUV94H4/kacyt13VkduuKv06v8DGrM/W8dni4yxb1JR/Pd6PY8d/5sN/DLQupPAbN05axc7kwfS5OZlf5w6SZYtFjQV0C7wyufmFtOy7mvS1PZk6dwOTRnSzOpLwUXa75sl/reDlh9qDgoJTdaTlLapERqFUUURYCL8vbIuKzGTyDOmrFNXX8fKlvDyxD+H10/n3J/uleAuXCeguFEcSm8UQ1SiV3OxQq6MIH7Z3i9nM4djOBKIi5HdJuI40BRwIjcwnLyfM6hjCh4WE5xMcc0CKt3A5KeAOBIfYsBUGWx1D+Kjc/EJO7muJtssFS+F6UsCFcCO7XRMUkoc9L4rc/EKr4wg/IwXcAaU0aGk9ieqJigjlytGbIK8uG3YcsTqO8DNSwB1QCrQUcFFNdrtm717z+/P63O0WpxH+xukCrpQKVkqtUUotLPr6OaXUfqXU2qKPK90X00IKkDWvRDVs3p1B054rWf/FIGolpPJ/f2lrdSThZ6oyjHASsBkouw7mFK31P10bybsoNBppgYuqe2p6KulrBxHWaCcZW9sRESajdoVrOdUCV0rFAVcBb7s3jheS2i2qacqj3ajfeRX56a0YPukXq+MIP+RsF8pU4BHAftb5CUqp9Uqpd5RS9VyazJtIF4qohvjGdfn4vYYAhMtUAuEGDgu4UmookK61PntDv5nAeUA34CAwuYLHj1VKpSilUo4c8b2r8EpppBkuqqt4waqVv0dYnET4I2da4P2AYUqp3cA8YIhS6n2t9WGttU1rbQdmA73Le7DWepbWOklrnRQbG+uy4J4io1BETQzuHk+rQb9wYEVv8gtsVscRfsZhAddaP661jtNaJwAjgB+11qOUUk3L3G044J/bvcsoFFEDC35KY+evPYhskUZYqMzoFa5Vk8viryilumHK225gnCsCeRuZyCNqYte+U1AQRe8hh4D2VscRfqZKBVxrnQwkFx3f6oY8QviVxLjaAHRoKwtZCdeTmZhOkHHgorrmzD8AQO+uMdYGEX5JCrgDSvrARTW9v2gzi+b0pn7nVdw+tJPVcYQfkgLugPSBi+p6/tUMKIhi+I2nZQSKcAsp4I5I7RbVNGpEBCrqGHOe7U9Uo8NccmcyGZmyPZ9wHSngTpA+cFEdz97Vi2OHo7j/n79SL34/P8wZTJOE40z8x6/Y7dIvJ2pOCrgTlHSCi2qKqR3BlAcv5Mi6XsyYv46w6Cxef+xCYtpt4D/fbLI6nvBxUsAdkdotXGTCjedzYntbRj+zlJN7E7ntyo58t2K31bGED5MC7gwlVVy4RlhoMO8+P4B7nl8PwKV9Eohoto23v/DPiczCvWSBYgdkHRThDm881o/2rdbxn4+Pk/JZH+4aHszTjTQ9eyri4+HCC+HYMQgOhtBQSEiAwYMhTFY1FGVIAXeG1HDhBhNvOp+JN4EadQUsfZx2Xdqwa1dTfv4ZZs489/7t2sGyZdCggeezCu8kXSgOaOk9EW504OQBaLOIpveN4PMFUaSmwtGj8Ntv8McfkJ4O+/bBvHmwfTu88ILViYU3kRa4E2QUinCXqNAoAE7ln6JuRF3AdJn07Xvm/W6+Gd57D2bPhh494JZbIET+9wY8aYELYaGYiBgSYxIJDnK81Ozs2dCpE9x2G7RuDa+9BmvXQm6u+3MK7yQF3BkyCkW40aXnXUpYsOOrk3Fx8Pvv8NlnEB8PDz4I3btDrVqmoN9+O/zvf7Bli3T9BQr5I8wBGYUi3E1V4Sp5UBBcc4352LoV1q2D1FTYtAkWLDDdLAAXXACjR5v7NW7sltjCC0gBd4bUcOFGuprXWNq1Mx/F8vNNl8rvv8M//wnjxsE998Dll8Prr5uhiMK/SBeKI/KnqPCAqrTCKxIWBr17w333wZ49sHEjPPooLF0KXbvC11+7IKjwKlLAnaCkD1z4GKXMBc8XX4Q1a+DkSXjgAatTCVeTAu6AXAwS7qbd/EuWmAixsabPvLiPXPgHKeBOkSou3Esp911oUQpWrIDwcHjpJbe9jLCAFHAHZBSKcJcCWwHL9i7jSM4Rt79WQoIZZpiWBqtWuf3lhIdIAXeG1HDhBi//8jL93+3Pp1s+LZmR6U4PPQR160KvXjBmDBw44PaXFG4mBdwR6T0RbtI3rnS+/ORLJ7v99c47z1zQvOce+PBDaNMG/vxnWLwYMjPd/vLCDaSAO0HWQhHusP3Y9pLjb7Z945HXTEw0Y8LXroVRo+Cbb8w48ZgYuP9+j0QQLiQF3AHZD1O4y9297ibniRwaRDZwai0UV2rXDt56Cw4ehC++gMsug2nTzNDDFSs8GkXUgNMFXCkVrJRao5RaeNb5h5RSWinV0PXxvITUcOEmkaGR5NnyCA8Ot+T1w8Ph6qtNl8rkyWa8+CWXwOOPmzVVhHerSgt8ErC57AmlVAvgT8BeV4byKtJ7Itxoz4k9nMo/RURIhKU56tUzE32WLoVBg+DVV6FzZxg7Ft54w7TSN292/DzCs5wq4EqpOOAq4O2zbpoCPIKflzmZiSnc5dnkZwH4Mu1Li5MYLVvCl1+aESpjxsB//gMTJphFsTp2NH3lV1wBv/5qdVIBzrfAp2IKtb34hFJqGLBfa72usgcqpcYqpVKUUilHjrh/vKuryUxM4U7jk8YD0KNpD4uTnKlRI5g1C06fNsV85Ur4179gxAgzjnzAANOHbrc7fi7hPg4LuFJqKJCutV5V5lwU8CTwjKPHa61naa2TtNZJsbGxNQprGWmBCzfpG9eXdg3akWfLszpKuZSCpk0hKQnuvhvefNNs7TZoEIwfDwMHyhBEKznTAu8HDFNK7QbmAUOA/wKJwLqi83HAaqVUEzfltIzMxBTuFh4STl6hdxbw8kRHw3ffwZw5Zunaa6+FrCyrUwUmhwVca/241jpOa50AjAB+1Fpfr7VupLVOKDq/D+ihtT7k3rhC+J/w4HCvbYFXJDjY9JHPmQM//wxduphp+sKzZBy4EB6UV5jH0ZyjHM05SkZOBhk5GWTmZbJo+yKro1XLbbfB99/DsWNmQtDKlVYnCixV2pFHa50MJJdzPsE1cbyTjEIRrmCz20iYlsChU+X/oXoq/xS1w2p7OFXNXXSRmdE5YoSZmr91q+k7F+4nW6o5IH3gwlUK7AUcOnWIuOg4HrnwEcAsI2uz22jToI1PFu9i/fvDyy/DrbfC+++bz8L9pIA7Q2q4cKF7e93LxD4TrY7hcrfcAs8+axbLuvpqM2ZcuJcUcEek90S4mLt34LFKUBB06AA5ORDl/tVxBXIR0ynSBy5cwRUbF3uztDRYtAiGDTMbLAv3kwLugPSBC+GcDRvAZoM777Q6SeCQAi6Eh2k/7ZcrXimjsNDaHIFECrgQHuLOjYu9QefO5vNPP1mbI5BIAXeC9IGL6njihydo8s8mPPnDk/y+73fybflWR3Kr9u2hQQN4/nn4+mur0wQGKeAOSB+4qK7l+5dzOPswLy97mQvmXECdl+oA/jsKpWFDWLYM8vNhwQKr0wQGGUboDKnhohrCgsPo1awXC29ZyPc7v2fJ7iUczz3OVW2vsjqa2xw/bj5nZ1ubI1AoT7YGkpKSdEpKisdezxXqd1rN8S1dCK51wjMv6JHuGg/9m3us68kVr+PgXbrM9+Ls+3mhvZCw4DAa1WrsfAoPNBbc+Ro2G+zfb463bDF7b4qaU0qt0lonnX1eWuAO3P9/NuYu+AW7U290ld+n0lu1qvQemvIKR9VHFrtzk+Yz0jjoenLJSIwKnsLlbxu63EOnHhhfN55W9Zwr4J5oS3nqNRo0gFat3P9agU5a4EII4eUqaoHLRUwhhPBRUsCFEMJHSQEXQggfJQVcCCF8lBRwIYTwUVLAhRDCR0kBF0IIHyUFXAghfJRHJ/IopY4Ae9z8Mg2BDDe/RnVJturx5mzg3fkkW/V4W7aWWuvYs096tIB7glIqpbwZS95AslWPN2cD784n2arHm7OVJV0oQgjho6SACyGEj/LHAj7L6gCVkGzV483ZwLvzSbbq8eZsJfyuD1wIIQKFP7bAhRAiIEgBF0IIH+UXBVwpdb5S6jel1Aal1JdKqeii839WSq0t82FXSnXzhmxFt3Utui216PYIT2arLJ9SKkEpdbrMz+5Nb8lW5vZ4pdQppdRD3pJNKdW7zM9snVJquBdl+5NSalXR+VVKqSGezuYgXwOl1E9F/6ave1O2otseV0ptV0ptVUpdZkW+c2itff4DWAkMKjoeA7xQzn26ADu9JRtmO7v1wPlFXzcAgr0oXwKw0Zv/XYH/AR8DD3lLNiAKCCk6bgqkF3/tBdm6A82KjjsD+73p3xWoBfQHxgOve1m2jsA6IBxIBHZY8f/17A+/aIED7YCfi46/A64v5z4jgQ89lqhURdkuBdZrrdcBaK2Paq1tXpTPG1SYTSl1LbATSPV8LKCCbFrrHK11YdH5CDy2g7RT2dZorQ8UnU8FIpRS4V6UL1tr/QuQa0GmYhX9zl0DzNNa52mtdwHbgd4W5DuDvxTwjcCwouMbgRbl3OdmrCngFWVrC2il1GKl1Gql1CMWZIPKf3aJSqk1SqklSqkBno9WfjalVC3gUeB5CzIVq/DnppTqo5RKBTYA48sUdMuzlXE9sEZrneexVKWcyWeVirI1B/4oc799Recs5TO70iulvgealHPTk5g/daYrpZ4BvgDyz3psHyBHa73Ri7KFYP5c7AXkAD8UbVz6g5fkOwjEa62PKqV6Ap8ppTpprbO8INvzwBSt9SmllCvjuCIbWuvlQCelVAfg30qpb7TWLm1V1vD/QyfgH5i/At2iJvncrZrZyvtFs34MttV9OG7ow2oLrDjr3BTgCW/KBowA3itz29PAw96Sr5zbkoEkb8gGLAV2F32cAI4BE7whWzm3/eQtP7eir+OANKCfVZkc/eyA0VjUB15RNuBx4PEyty0GLrA6o190oSilGhV9DgKeAt4sc1sQ5k+heV6WbTHQVSkVpZQKAQYBm7wln1IqVikVXHTcCmiD6XO2PJvWeoDWOkFrnQBMBf6utfboqIVKfm6JRf+eKKVaYvpUd3tJthjgK0whWubJTM7k8waVZPsCGKGUCldKJWL+P6ywJmUpvyjgwEilVBqwBTgAvFvmtoHAPq21R4tPGeVm01ofB17DXPVeC6zWWn/lLfkwP7f1Sql1wAJMX+4xL8nmDSrK1h9Yp5RaC3wK3KO19vSypBVlmwC0Bp4uM9SxkYezVZYPpdRuzP+L0UqpfUqpjt6QTWudCszHNLIWAfdqawYdnEGm0gshhI/ylxa4EEIEHCngQgjho6SACyGEj5ICLoQQPkoKuBBC+Cgp4EII4aOkgAshhI/6fyrQz6UeGHCUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  5706459 3201096 0.46360486351433333\n",
      "compare to Census 5706494 Leip has Trump + Biden = 3201142.0 0.46360486351433333\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 5706494\n",
    "atlasTrump = 1484065.\n",
    "atlasBiden = 1717077.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 5706459 5706459.0\n",
      "state Trumpers before, after slice opn are 1484043 1484043.0\n",
      "state Bideners before, after slice opn are 1717053 1717053.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 90 grids for 8 districts\n",
      "starting grid no 0 at x = -96.96423138888888, y = 43.77441565000001 \n",
      "starting grid no 5 at x = -96.96423138888888, y = 46.52496215000001 \n",
      "starting grid no 10 at x = -96.41450816666666, y = 43.774415650000016 \n",
      "starting grid no 15 at x = -96.41450816666666, y = 46.52496215 \n",
      "starting grid no 20 at x = -95.86478494444444, y = 43.77441565000001 \n",
      "starting grid no 25 at x = -95.86478494444444, y = 46.52496215000001 \n",
      "starting grid no 30 at x = -95.31506172222224, y = 43.77441565000001 \n",
      "starting grid no 35 at x = -95.31506172222224, y = 46.52496215000001 \n",
      "starting grid no 40 at x = -94.76533850000001, y = 43.774415650000016 \n",
      "starting grid no 45 at x = -94.76533850000001, y = 46.52496215 \n",
      "starting grid no 50 at x = -94.21561527777777, y = 43.77441565000001 \n",
      "starting grid no 55 at x = -94.21561527777777, y = 46.52496215000001 \n",
      "starting grid no 60 at x = -93.66589205555555, y = 43.77441565000001 \n",
      "starting grid no 65 at x = -93.66589205555555, y = 46.52496215000001 \n",
      "starting grid no 70 at x = -93.11616883333335, y = 43.774415650000016 \n",
      "starting grid no 75 at x = -93.11616883333335, y = 46.52496215 \n",
      "starting grid no 80 at x = -92.56644561111112, y = 43.77441565000001 \n",
      "starting grid no 85 at x = -92.56644561111112, y = 46.52496215000001 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 222000.884624088 7117153.6351951035 85.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 8   #MN congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uBO55he/tAfYAnMmWLsKtWXqI2dcBkc++eFYvcc/YeLSXuOdve6GXuH/y7K81j3nXxsPNYwJcefbjvcTtQoAM7UZakrOBPwV+t6p+fOL3x+uXlwG25VWL9eklDV4aj9lOMlfRTbKJUcG9varu7CYlSerIkE6OSBLg08C+qvqj7lKSpK60n50wyTx7L1wFvA94e5IHx9e7OspLkjrRxd4LXZq5p1tV/4d+7lFJ0vQWrKfrijRJw1UDnL0gSQttsWquRVfSsA1qypgkLTyLriQ1UsB0B1M2Y9GVNFihHF6QpKZWF6ur68GUkobr2PDCpGuCLvcPt6cradA6Gl7obP9we7qShq2DkyO63D/cnq6kAZt6w5sdSe497vHyeFvan3Gq/cOnYdGVNFzTnwZ8qKp2T3rRpP3Dp2HRlTRoXU0Z62r/cIuupGHroOh2uX+4N9IkDVcBqzX5mqyz/cPt6UoasG5Ojuhy//DTouhecG77E2NXq5/93Zc29LP6ZkPr7ffHHnj+4l7i/uTopuYxX7ftqeYxATZlpZe4nXEZsCQ1UsDKYi0DtuhKGrCCsuhKUjsOL0hSI8dmLywQi66kYbOnK0kNWXQlqZEqWFmsKW8WXUnDZk9Xkhqy6EpSK1PvrdCMRVfScBWUiyMkqSGXAUtSI1ULdwS7RVfSsHkjTZLaKXu6ktRKN5uYd8miK2m43PBGktopoBZsGfBcB1MmuSbJXyf52yQf7SopSepEjTcxn3RNoat6N3PRTbIE/GfgnwOvB65P8vpZ25Ok9VCrNfGapMt6N09P943A31bVY1X1MvBfgffO0Z4kda+bnm5n9W6eMd2dwBPHPd4P/NqJL0qyB9gzfvjSl2rvQ3PEnM07mkcE2AEc6iVyP/y86+yulsF+Wl8/2380bwMv8OwXv1R7d0zx0jOT3Hvc4+WqWj7u8VT1bhrzFN1XOmP8Z/rp48SXAZLcW1W754j5c+N0+qzg5x2yn+fPWlXXdNTUVPVuGvMML+wHLj7u8S7gh3O0J0mLqrN6N0/R/b/Aa5NcmmQzcB3wuTnak6RF1Vm9m3l4oaqOJvm3wBeBJeDWqnp4wtuWJ3x/SE6nzwp+3iE7nT7rK5qx3r2i1IItkZOkIZtrcYQkaW0supLUUJOiezotF05ycZK/SLIvycNJbuo7p/WWZCnJA0k+33cu6y3JeUn2Jnl0/DN+c985rZckHx7/Dj+U5I4kZ/ad0xCse9E9DZcLHwV+r6p+CXgT8DsD/7wANwH7+k6ikU8CX6iq1wG/wkA/d5KdwIeA3VV1BaObR9f1m9UwtOjpnlbLhavqQFXdP/76BUb/U+7sN6v1k2QX8G7glr5zWW9JtgFvBT4NUFUvV9VzvSa1vjYCZyXZCGzBefidaFF0X2n53GCL0PGSXAJcCdzTcyrr6RPAR4DF2p5/fVwGPA18ZjycckuSrX0ntR6q6gfAx4HHgQPA81XV40rk4WhRdDtbPvfzJMnZwJ8Cv1tVP+47n/WQ5FrgYFXd13cujWwE3gB8qqquBA4Dg7xHkWQ7o3+RXgpcBGxNckO/WQ1Di6J72i0XTrKJUcG9varu7DufdXQV8J4k32c0bPT2JLf1m9K62g/sr6pj/3LZy6gID9E7gO9V1dNVdQS4E3hLzzkNQouie1otF04SRmN++6rqj/rOZz1V1ceqaldVXcLo5/qVqhpsb6iqngSeSHL5+KmrgUd6TGk9PQ68KcmW8e/01Qz0pmFr635cT5fL535OXAW8D/h2kgfHz/2Hqvqf/aWkDn0QuH3cgXgMeH/P+ayLqronyV7gfkYzch7A5cCdcBmwJDXkijRJasiiK0kNWXQlqSGLriQ1ZNGVpIYsupLUkEVXkhr6/+CWMUG3zX9lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9950574466045492e-07 3.693415499997354e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 4706 tracts\n",
      "I am working on tract number 20 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 26\n",
      "I am working on tract number 40 of 4706 tracts\n",
      "I am working on tract number 60 of 4706 tracts\n",
      "I am working on tract number 80 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 98 1 274128.1738346493 2.3787\n",
      "8 yoyos for tract,wedge,wedgePop,r= 98 1 99238.37289927056 1.1893\n",
      "9 yoyos for tract,wedge,wedgePop,r= 98 1 274116.3008508998 2.3785\n",
      "10 yoyos for tract,wedge,wedgePop,r= 98 1 127681.91576739491 1.7839\n",
      "11 yoyos for tract,wedge,wedgePop,r= 98 1 236210.04427755822 2.0812\n",
      "12 yoyos for tract,wedge,wedgePop,r= 98 1 142363.38077967044 1.9326\n",
      "I am working on tract number 100 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 101 7.0 0 87.0 1.3166 184192.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 101 8.0 0 87.0 1.2849 184192.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 101 9.0 0 87.0 1.254 184192.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 101 10.0 0 87.0 1.2238 184192.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 101 11.0 0 87.0 1.1943 184192.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 101 12.0 0 87.0 1.1655 184192.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 101 13.0 0 87.0 1.1375 184192.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 101 14.0 0 87.0 1.1101 184192.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 101 15.0 0 87.0 1.0834 184192.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 108 0 221592.3485983027 2.3037\n",
      "8 yoyos for tract,wedge,wedgePop,r= 108 0 82041.25040857319 1.1518\n",
      "9 yoyos for tract,wedge,wedgePop,r= 108 0 269869.76944001496 2.6205\n",
      "10 yoyos for tract,wedge,wedgePop,r= 108 0 105634.07585162402 1.8861\n",
      "11 yoyos for tract,wedge,wedgePop,r= 108 0 207744.31540142256 2.2533\n",
      "12 yoyos for tract,wedge,wedgePop,r= 108 0 117253.69247448041 2.0697\n",
      "12 yoyos for tract,wedge,wedgePop,r= 108 0 136809.10643777082 2.1615\n",
      "12 yoyos for tract,wedge,wedgePop,r= 108 0 151790.59010102422 2.2074\n",
      "12 yoyos for tract,wedge,wedgePop,r= 108 0 170974.75689725886 2.2304\n",
      "13 yoyos for tract,wedge,wedgePop,r= 108 0 192333.1438909375 2.2418\n",
      "13 yoyos for tract,wedge,wedgePop,r= 108 0 181687.52273827363 2.2361\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 110 9.0 0 121.1 2.2346 182517.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 113 9.0 0 83.5 1.4338 196072.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 113 10.0 0 83.5 1.3341 196072.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 113 11.0 0 83.5 1.2414 196072.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 115 6.0 3 90.0 2.7943 220330.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 115 3 220330.1614389162 3.0107\n",
      "I am working on tract number 120 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 131 2 208489.57220535117 2.237\n",
      "8 yoyos for tract,wedge,wedgePop,r= 131 2 67304.05632529888 1.1185\n",
      "9 yoyos for tract,wedge,wedgePop,r= 131 2 210874.12455639426 2.2487\n",
      "10 yoyos for tract,wedge,wedgePop,r= 131 2 88798.79383459182 1.6836\n",
      "10 yoyos for tract,wedge,wedgePop,r= 131 2 100014.04519562595 1.9662\n",
      "10 yoyos for tract,wedge,wedgePop,r= 131 2 120379.15497958224 2.1075\n",
      "10 yoyos for tract,wedge,wedgePop,r= 131 2 148153.17722447094 2.1781\n",
      "11 yoyos for tract,wedge,wedgePop,r= 131 2 200686.01663416 2.2134\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 133 6.0 0 124.4 3.0936 204307.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 133 7.0 0 124.4 2.7886 204307.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 133 12.0 0 124.4 3.025 204307.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 133 0 204307.68360894884 2.8588\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 139 4.0 3 70.7 1.5387 298747.8\n",
      "I am working on tract number 140 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 140 9.0 0 130.4 2.9195 186537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 140 10.0 0 130.4 2.8221 186537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 140 11.0 0 130.4 2.7279 186537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 140 12.0 0 130.4 2.6368 186537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 140 13.0 0 130.4 2.5488 186537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 140 14.0 0 130.4 2.4637 186537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 140 15.0 0 130.4 2.3814 186537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 140 20.0 0 130.4 2.5331 186537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 140 21.0 0 130.4 2.4485 186537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 142 9.0 0 129.8 3.3941 202043.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 142 10.0 0 129.8 3.086 202043.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 142 11.0 0 129.8 2.8058 202043.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 142 16.0 0 129.8 3.2164 202043.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 142 17.0 0 129.8 2.9244 202043.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 142 22.0 0 129.8 2.994 202043.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 142 0 202043.46452424448 3.0495\n",
      "7 yoyos for tract,wedge,wedgePop,r= 144 3 318676.070388225 2.8012\n",
      "8 yoyos for tract,wedge,wedgePop,r= 144 3 112309.59176067516 1.4006\n",
      "9 yoyos for tract,wedge,wedgePop,r= 144 3 320431.24108648 2.8374\n",
      "10 yoyos for tract,wedge,wedgePop,r= 144 3 138138.30046724516 2.119\n",
      "11 yoyos for tract,wedge,wedgePop,r= 144 3 252431.52049714077 2.4782\n",
      "12 yoyos for tract,wedge,wedgePop,r= 144 3 173842.68054028694 2.2986\n",
      "13 yoyos for tract,wedge,wedgePop,r= 144 3 246517.3905266004 2.3884\n",
      "13 yoyos for tract,wedge,wedgePop,r= 144 3 218555.06844811028 2.3435\n",
      "13 yoyos for tract,wedge,wedgePop,r= 144 3 184978.36204517493 2.3211\n",
      "7 yoyos for tract,wedge,wedgePop,r= 147 0 252903.41442636895 4.3415\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 152 7.0 3 90.0 2.7196 217600.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 152 8.0 3 90.0 2.3331 217600.7\n",
      "I am working on tract number 160 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 161 7.0 0 90.0 2.2206 235579.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 166 1 256580.91528403887 2.6565\n",
      "8 yoyos for tract,wedge,wedgePop,r= 166 1 114034.584659379 1.3282\n",
      "9 yoyos for tract,wedge,wedgePop,r= 166 1 256749.78110710133 2.6675\n",
      "10 yoyos for tract,wedge,wedgePop,r= 166 1 135793.97357718914 1.9979\n",
      "11 yoyos for tract,wedge,wedgePop,r= 166 1 249217.9009681003 2.3327\n",
      "12 yoyos for tract,wedge,wedgePop,r= 166 1 164326.19084738073 2.1653\n",
      "13 yoyos for tract,wedge,wedgePop,r= 166 1 228321.99554426083 2.249\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 172 10.0 0 118.0 2.7531 219798.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 172 15.0 0 118.0 2.9932 219798.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 172 20.0 0 118.0 2.6556 219798.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 172 0 214940.6416113807 2.3074\n",
      "8 yoyos for tract,wedge,wedgePop,r= 172 0 89696.70755214807 1.1537\n",
      "9 yoyos for tract,wedge,wedgePop,r= 172 0 219798.1162003336 2.6093\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 174 9.0 0 117.1 2.7192 212644.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 174 14.0 0 117.1 3.0647 212644.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 174 15.0 0 117.1 2.6775 212644.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 174 20.0 0 117.1 3.2663 212644.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 174 21.0 0 117.1 2.8536 212644.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 174 22.0 0 117.1 2.493 212644.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 174 0 212644.31482534565 3.9171\n",
      "loop31.0, tr174,wedgePops711676.53, 116750.5, 199049.6, 198718.9, 197157.6, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0111 \n",
      "   targetWP, latest drx4 are tWP,dr, 198852.3,-1.9586, 198852.3,-0.0013, 198852.3,0.0003, 198852.3,0.0281\n",
      "I am working on tract number 180 of 4706 tracts\n",
      "I am working on tract number 200 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 212\n",
      "I am working on tract number 220 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 229\n",
      "I am working on tract number 240 of 4706 tracts\n",
      "I am working on tract number 260 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 261 3 315503.8435479173 3.0597\n",
      "8 yoyos for tract,wedge,wedgePop,r= 261 3 30232.39492177934 1.5299\n",
      "9 yoyos for tract,wedge,wedgePop,r= 261 3 316675.9241399104 3.0793\n",
      "10 yoyos for tract,wedge,wedgePop,r= 261 3 145361.84475094252 2.3046\n",
      "10 yoyos for tract,wedge,wedgePop,r= 261 3 189586.208207022 2.6919\n",
      "10 yoyos for tract,wedge,wedgePop,r= 261 3 220622.07479563032 2.8856\n",
      "11 yoyos for tract,wedge,wedgePop,r= 261 3 300604.29236692074 2.9825\n",
      "12 yoyos for tract,wedge,wedgePop,r= 261 3 235960.96336515492 2.934\n",
      "we have 2 non-opposing shorted wedges for tract no 262\n",
      "we have 2 non-opposing shorted wedges for tract no 274\n",
      "we have 2 non-opposing shorted wedges for tract no 276\n",
      "I am working on tract number 280 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 281\n",
      "I am working on tract number 300 of 4706 tracts\n",
      "I am working on tract number 320 of 4706 tracts\n",
      "I am working on tract number 340 of 4706 tracts\n",
      "I am working on tract number 360 of 4706 tracts\n",
      "I am working on tract number 380 of 4706 tracts\n",
      "I am working on tract number 400 of 4706 tracts\n",
      "I am working on tract number 420 of 4706 tracts\n",
      "I am working on tract number 440 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 441 0 206199.68168445685 2.6453\n",
      "8 yoyos for tract,wedge,wedgePop,r= 441 0 85494.25165634285 1.3227\n",
      "9 yoyos for tract,wedge,wedgePop,r= 441 0 214375.8652585724 2.7353\n",
      "10 yoyos for tract,wedge,wedgePop,r= 441 0 134644.78851682437 2.029\n",
      "10 yoyos for tract,wedge,wedgePop,r= 441 0 150288.41029701926 2.3822\n",
      "11 yoyos for tract,wedge,wedgePop,r= 441 0 203779.1281505983 2.5587\n",
      "11 yoyos for tract,wedge,wedgePop,r= 441 0 193515.17714575477 2.4704\n",
      "12 yoyos for tract,wedge,wedgePop,r= 441 0 164638.94049829198 2.4263\n",
      "we have 2 non-opposing shorted wedges for tract no 446\n",
      "we have 2 non-opposing shorted wedges for tract no 448\n",
      "I am working on tract number 460 of 4706 tracts\n",
      "I am working on tract number 480 of 4706 tracts\n",
      "I am working on tract number 500 of 4706 tracts\n",
      "I am working on tract number 520 of 4706 tracts\n",
      "I am working on tract number 540 of 4706 tracts\n",
      "I am working on tract number 560 of 4706 tracts\n",
      "I am working on tract number 580 of 4706 tracts\n",
      "I am working on tract number 600 of 4706 tracts\n",
      "I am working on tract number 620 of 4706 tracts\n",
      "I am working on tract number 640 of 4706 tracts\n",
      "I am working on tract number 660 of 4706 tracts\n",
      "I am working on tract number 680 of 4706 tracts\n",
      "I am working on tract number 700 of 4706 tracts\n",
      "I am working on tract number 720 of 4706 tracts\n",
      "I am working on tract number 740 of 4706 tracts\n",
      "I am working on tract number 760 of 4706 tracts\n",
      "I am working on tract number 780 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 787\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 3.0 1 63.2 1.5857 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 4.0 1 63.2 1.4774 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 5.0 1 63.2 1.3766 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 6.0 1 63.2 1.3556 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 7.0 1 63.2 1.3348 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 8.0 1 63.2 1.3144 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 9.0 1 63.2 1.2944 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 10.0 1 63.2 1.2746 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 11.0 1 63.2 1.2551 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 12.0 1 63.2 1.2359 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 13.0 1 63.2 1.217 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 14.0 1 63.2 1.1984 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 15.0 1 63.2 1.1801 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 16.0 1 63.2 1.1621 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 17.0 1 63.2 1.1443 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 18.0 1 63.2 1.1268 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 19.0 1 63.2 1.1096 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 20.0 1 63.2 1.0927 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 21.0 1 63.2 1.076 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 22.0 1 63.2 1.0595 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 23.0 1 63.2 1.0433 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 24.0 1 63.2 1.0274 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 25.0 1 63.2 1.0117 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 26.0 1 63.2 0.9962 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 27.0 1 63.2 0.981 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 28.0 1 63.2 0.966 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 29.0 1 63.2 0.9513 232042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 30.0 1 63.2 0.9367 232042.3\n",
      "loop31.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0143, 227338.4,-0.002, 227338.4,0.6169\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 31.0 1 63.2 0.9224 232042.3\n",
      "loop32.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0141, 227338.4,-0.002, 227338.4,0.6169\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 32.0 1 63.2 0.9083 232042.3\n",
      "loop33.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0139, 227338.4,-0.002, 227338.4,0.6169\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 33.0 1 63.2 0.8944 232042.3\n",
      "loop34.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0137, 227338.4,-0.002, 227338.4,0.6169\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 34.0 1 63.2 0.8808 232042.3\n",
      "loop35.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0135, 227338.4,-0.002, 227338.4,0.6169\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 35.0 1 63.2 0.8673 232042.3\n",
      "loop36.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0133, 227338.4,-0.002, 227338.4,0.6169\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 36.0 1 63.2 0.854 232042.3\n",
      "loop37.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0131, 227338.4,-0.002, 227338.4,0.6169\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 37.0 1 63.2 0.841 232042.3\n",
      "loop38.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0129, 227338.4,-0.002, 227338.4,0.6169\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 38.0 1 63.2 0.8281 232042.3\n",
      "loop39.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0127, 227338.4,-0.002, 227338.4,0.6169\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.6456 2.8498 79098.5571 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.81549 232042.2895 232042.2895 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.09403 228655.9597 227352.3965 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 4.14368 164792.5544 179531.9468 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 788 39.0 1 63.2 0.8155 232042.3\n",
      "loop40.0, tr788,wedgePops718025.19, 79098.6, 232042.3, 227352.4, 179531.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 227338.4,1.6355, 227338.4,-0.0125, 227338.4,-0.002, 227338.4,0.6169\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.6456 2.8498 79098.5571 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.80302 232042.2895 232042.2895 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.09403 228655.9597 227352.3965 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 4.14368 164792.5544 179531.9468 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 788, giving up w pop 718025.1898965043\n",
      "we have 2 non-opposing shorted wedges for tract no 789\n",
      "we have 2 non-opposing shorted wedges for tract no 794\n",
      "we have 2 non-opposing shorted wedges for tract no 795\n",
      "we have 2 non-opposing shorted wedges for tract no 796\n",
      "we have 2 non-opposing shorted wedges for tract no 797\n",
      "I am working on tract number 800 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 802 5.0 0 89.4 0.6574 181191.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 802 6.0 0 89.4 0.6495 181191.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 802 7.0 0 89.4 0.6418 181191.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 817 1 283435.29570642463 2.4761\n",
      "8 yoyos for tract,wedge,wedgePop,r= 817 1 77421.62837828812 1.238\n",
      "9 yoyos for tract,wedge,wedgePop,r= 817 1 283429.3904963292 2.476\n",
      "9 yoyos for tract,wedge,wedgePop,r= 817 1 204076.8811068673 1.857\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 1 94830.96445111412 1.5475\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 1 105242.78212886427 1.7023\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 1 128596.32148025889 1.7796\n",
      "I am working on tract number 820 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 827 5.0 1 90.0 2.0627 185267.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 835 0 200758.04040433094 2.1273\n",
      "8 yoyos for tract,wedge,wedgePop,r= 835 0 59849.55416724493 1.0637\n",
      "9 yoyos for tract,wedge,wedgePop,r= 835 0 204106.91951002157 2.1417\n",
      "10 yoyos for tract,wedge,wedgePop,r= 835 0 81239.17002717068 1.6027\n",
      "10 yoyos for tract,wedge,wedgePop,r= 835 0 92621.63290036817 1.8722\n",
      "10 yoyos for tract,wedge,wedgePop,r= 835 0 116202.66514494628 2.0069\n",
      "10 yoyos for tract,wedge,wedgePop,r= 835 0 139152.97966441562 2.0743\n",
      "11 yoyos for tract,wedge,wedgePop,r= 835 0 185072.29299769088 2.108\n",
      "12 yoyos for tract,wedge,wedgePop,r= 835 0 155203.21996813978 2.0911\n",
      "12 yoyos for tract,wedge,wedgePop,r= 835 0 171373.36381018467 2.0996\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 5.0 3 90.0 2.0684 229197.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 836 0 217827.7977799216 1.9195\n",
      "8 yoyos for tract,wedge,wedgePop,r= 836 0 82546.2939836545 0.9598\n",
      "9 yoyos for tract,wedge,wedgePop,r= 836 0 219085.47749188112 1.9307\n",
      "10 yoyos for tract,wedge,wedgePop,r= 836 0 99528.22086215284 1.4452\n",
      "10 yoyos for tract,wedge,wedgePop,r= 836 0 113177.76766797567 1.688\n",
      "10 yoyos for tract,wedge,wedgePop,r= 836 0 138385.64785406186 1.8093\n",
      "10 yoyos for tract,wedge,wedgePop,r= 836 0 168557.4002985377 1.87\n",
      "11 yoyos for tract,wedge,wedgePop,r= 836 0 211252.44528443262 1.9004\n",
      "11 yoyos for tract,wedge,wedgePop,r= 836 0 195387.9109946641 1.8852\n",
      "11 yoyos for tract,wedge,wedgePop,r= 836 0 183102.97185635893 1.8776\n",
      "7 yoyos for tract,wedge,wedgePop,r= 837 3 268195.2360161102 2.5599\n",
      "8 yoyos for tract,wedge,wedgePop,r= 837 3 83175.52533280122 1.28\n",
      "9 yoyos for tract,wedge,wedgePop,r= 837 3 268310.4053686982 2.5639\n",
      "10 yoyos for tract,wedge,wedgePop,r= 837 3 113342.81243027543 1.9219\n",
      "11 yoyos for tract,wedge,wedgePop,r= 837 3 249178.89436294904 2.2429\n",
      "12 yoyos for tract,wedge,wedgePop,r= 837 3 141378.2789780734 2.0824\n",
      "13 yoyos for tract,wedge,wedgePop,r= 837 3 183305.91409088054 2.1626\n",
      "14 yoyos for tract,wedge,wedgePop,r= 837 3 159417.48574927542 2.1225\n",
      "14 yoyos for tract,wedge,wedgePop,r= 837 3 170980.72619086475 2.1426\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 839 5.0 0 90.0 2.3462 221866.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 839 10.0 0 90.0 2.3943 221866.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 839 15.0 0 90.0 2.3851 221866.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 839 0 221866.25456355314 2.6506\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 839 25.0 0 83.6 2.3739 223776.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 839 30.0 0 83.6 2.4026 223776.0\n",
      "loop31.0, tr839,wedgePops751977.5, 216951.5, 178197.8, 178213.6, 178614.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?3223 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.3867, 178326.8,0.0014, 178326.8,0.0003, 178326.8,-0.0009\n",
      "loop32.0, tr839,wedgePops611819.47, 76793.4, 178197.8, 178213.6, 178614.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?3223 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-1.008, 178326.8,0.0014, 178326.8,0.0003, 178326.8,-0.0009\n",
      "loop33.0, tr839,wedgePops634067.82, 99041.8, 178197.8, 178213.6, 178614.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4223 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.6301, 178326.8,0.0014, 178326.8,0.0003, 178326.8,-0.0009\n",
      "loop34.0, tr839,wedgePops758802.04, 223776.0, 178197.8, 178213.6, 178614.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4223 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.0389, 178326.8,0.0014, 178326.8,0.0003, 178326.8,-0.0009\n",
      "loop35.0, tr839,wedgePops758802.04, 223776.0, 178197.8, 178213.6, 178614.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5223 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.2598, 178326.8,0.0014, 178326.8,0.0003, 178326.8,-0.0009\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 839 35.0 0 83.6 2.4171 223776.0\n",
      "loop36.0, tr839,wedgePops752704.13, 217678.1, 178197.8, 178213.6, 178614.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5223 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.3891, 178326.8,0.0014, 178326.8,0.0003, 178326.8,-0.0009\n",
      "loop37.0, tr839,wedgePops611964.08, 76938.1, 178197.8, 178213.6, 178614.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5223 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-1.014, 178326.8,0.0014, 178326.8,0.0003, 178326.8,-0.0009\n",
      "loop38.0, tr839,wedgePops634368.68, 99342.7, 178197.8, 178213.6, 178614.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6223 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.6311, 178326.8,0.0014, 178326.8,0.0003, 178326.8,-0.0009\n",
      "loop39.0, tr839,wedgePops758802.04, 223776.0, 178197.8, 178213.6, 178614.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6223 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.0331, 178326.8,0.0014, 178326.8,0.0003, 178326.8,-0.0009\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.6782 99342.6565 223776.013 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.17237 177447.2049 178197.7737 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.29487 177709.7623 178213.6171 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.37806 180105.6257 178614.6313 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 yoyos for tract,wedge,wedgePop,r= 839 0 223776.01297055013 2.6782\n",
      "loop40.0, tr839,wedgePops620918.5, 85892.5, 178197.8, 178213.6, 178614.6, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 209138.3,-1.3391, 209138.3,0.0014, 209138.3,0.0003, 209138.3,-0.0009\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.3391 223776.013 85892.4734 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.17237 177447.2049 178197.7737 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.29487 177709.7623 178213.6171 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.37806 180105.6257 178614.6313 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 839, giving up w pop 620918.4954904437\n",
      "I am working on tract number 840 of 4706 tracts\n",
      "I am working on tract number 860 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 863 1 285144.85553729685 1.9601\n",
      "8 yoyos for tract,wedge,wedgePop,r= 863 1 124430.70404435715 0.98\n",
      "9 yoyos for tract,wedge,wedgePop,r= 863 1 287567.5259977544 2.0072\n",
      "10 yoyos for tract,wedge,wedgePop,r= 863 1 154819.9062017072 1.4936\n",
      "10 yoyos for tract,wedge,wedgePop,r= 863 1 199895.07240965107 1.7504\n",
      "11 yoyos for tract,wedge,wedgePop,r= 863 1 278589.371709496 1.8788\n",
      "11 yoyos for tract,wedge,wedgePop,r= 863 1 267224.60789848655 1.8146\n",
      "11 yoyos for tract,wedge,wedgePop,r= 863 1 256004.9634941004 1.7825\n",
      "11 yoyos for tract,wedge,wedgePop,r= 863 1 226034.99984622217 1.7664\n",
      "12 yoyos for tract,wedge,wedgePop,r= 863 1 208333.37887527188 1.7584\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 867 4.0 0 137.6 1.8033 193677.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 867 5.0 0 137.6 1.6939 193677.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 867 6.0 0 137.6 1.5911 193677.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 867 7.0 0 137.6 1.4946 193677.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 867 7.0 1 42.4 2.8167 222186.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 867 8.0 0 137.6 1.4039 193677.5\n",
      "we have 2 non-opposing shorted wedges for tract no 869\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 871 6.0 0 89.7 3.3284 178937.5\n",
      "we have 2 non-opposing shorted wedges for tract no 878\n",
      "we have 2 non-opposing shorted wedges for tract no 879\n",
      "I am working on tract number 880 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 883 8.0 3 54.3 4.7722 347634.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 883 17.0 3 54.3 4.6839 347634.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 883 3 347634.3535431316 4.5937\n",
      "I am working on tract number 900 of 4706 tracts\n",
      "I am working on tract number 920 of 4706 tracts\n",
      "I am working on tract number 940 of 4706 tracts\n",
      "I am working on tract number 960 of 4706 tracts\n",
      "I am working on tract number 980 of 4706 tracts\n",
      "I am working on tract number 1000 of 4706 tracts\n",
      "I am working on tract number 1020 of 4706 tracts\n",
      "I am working on tract number 1040 of 4706 tracts\n",
      "I am working on tract number 1060 of 4706 tracts\n",
      "I am working on tract number 1080 of 4706 tracts\n",
      "I am working on tract number 1100 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1109 7.0 0 99.7 0.9793 179762.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1109 8.0 0 99.7 0.9735 179762.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1109 9.0 0 99.7 0.9676 179762.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1109 10.0 0 99.7 0.9618 179762.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1111 0 254849.85036888666 2.6438\n",
      "loop31.0, tr1111,wedgePops598757.4, 135187.9, 78440.9, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,-0.9629, 192706.5,0.0002, 192706.5,0.0004\n",
      "loop32.0, tr1111,wedgePops642218.24, 135187.9, 121901.8, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0611 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,0.6022, 192706.5,0.0002, 192706.5,0.0004\n",
      "loop33.0, tr1111,wedgePops765168.96, 135187.9, 244852.5, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0611 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,0.5242, 192706.5,0.0002, 192706.5,0.0004\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1111 1 244852.4881291665 2.0893\n",
      "loop34.0, tr1111,wedgePops603881.79, 135187.9, 83565.3, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0711 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,-1.0447, 192706.5,0.0002, 192706.5,0.0004\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1111 1 83565.31654908479 1.0447\n",
      "loop35.0, tr1111,wedgePops765168.04, 135187.9, 244851.6, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0811 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,1.0447, 192706.5,0.0002, 192706.5,0.0004\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1111 1 244851.56903732035 2.0893\n",
      "loop36.0, tr1111,wedgePops642400.18, 135187.9, 122083.7, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0911 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,-0.5223, 192706.5,0.0002, 192706.5,0.0004\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1111 1 122083.70942566855 1.567\n",
      "loop37.0, tr1111,wedgePops749880.28, 135187.9, 229563.8, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01011 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,0.2612, 192706.5,0.0002, 192706.5,0.0004\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1111 1 229563.81036113738 1.8282\n",
      "loop38.0, tr1111,wedgePops672749.19, 135187.9, 152432.7, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01111 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,-0.1306, 192706.5,0.0002, 192706.5,0.0004\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1111 1 152432.7209665235 1.6976\n",
      "loop39.0, tr1111,wedgePops739384.78, 135187.9, 219068.3, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01211 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,0.0653, 192706.5,0.0002, 192706.5,0.0004\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.78067 113457.6417 135187.9415 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.76288 152432.721 219068.3077 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.352 192063.7185 192580.8242 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.31175 191681.6366 192547.7074 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 yoyos for tract,wedge,wedgePop,r= 1111 1 219068.30771553353 1.7629\n",
      "loop40.0, tr1111,wedgePops727959.99, 135187.9, 207643.5, 192580.8, 192547.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01311 \n",
      "   targetWP, latest drx4 are tWP,dr, 192706.5,1.0805, 192706.5,-0.0326, 192706.5,0.0002, 192706.5,0.0004\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.78067 113457.6417 135187.9415 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.73023 219068.3077 207643.5139 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.352 192063.7185 192580.8242 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.31175 191681.6366 192547.7074 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1111, giving up w pop 727959.9870243001\n",
      "I am working on tract number 1120 of 4706 tracts\n",
      "I am working on tract number 1140 of 4706 tracts\n",
      "I am working on tract number 1160 of 4706 tracts\n",
      "I am working on tract number 1180 of 4706 tracts\n",
      "I am working on tract number 1200 of 4706 tracts\n",
      "I am working on tract number 1220 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1230\n",
      "I am working on tract number 1240 of 4706 tracts\n",
      "I am working on tract number 1260 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1262\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1274 14.0 2 90.0 2.4436 232575.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1274 15.0 2 90.0 1.9877 232575.1\n",
      "I am working on tract number 1280 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1291 5.0 1 40.5 1.7932 343111.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1291 10.0 1 40.5 1.5675 343111.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1293 7.0 1 39.7 1.6084 325644.1\n",
      "I am working on tract number 1300 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1300\n",
      "we have 2 non-opposing shorted wedges for tract no 1301\n",
      "we have 2 non-opposing shorted wedges for tract no 1302\n",
      "we have 2 non-opposing shorted wedges for tract no 1304\n",
      "we have 2 non-opposing shorted wedges for tract no 1305\n",
      "we have 2 non-opposing shorted wedges for tract no 1306\n",
      "I am working on tract number 1320 of 4706 tracts\n",
      "I am working on tract number 1340 of 4706 tracts\n",
      "I am working on tract number 1360 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 5.0 1 38.5 2.1846 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 6.0 1 38.5 2.1511 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 7.0 1 38.5 2.118 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 8.0 1 38.5 2.0855 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 9.0 1 38.5 2.0534 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 10.0 1 38.5 2.0218 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 11.0 1 38.5 1.9908 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 12.0 1 38.5 1.9602 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 13.0 1 38.5 1.9301 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 14.0 1 38.5 1.9004 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 15.0 1 38.5 1.8712 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 16.0 1 38.5 1.8424 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 17.0 1 38.5 1.8141 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 18.0 1 38.5 1.7862 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 19.0 1 38.5 1.7588 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 20.0 1 38.5 1.7318 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 25.0 1 38.5 1.7773 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 26.0 1 38.5 1.7499 308622.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 27.0 1 38.5 1.723 308622.8\n",
      "loop31.0, tr1361,wedgePops719597.64, 17312.0, 308174.3, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0320 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,0.1154, 302331.2,-0.0028, 302331.2,1.1933\n",
      "loop32.0, tr1361,wedgePops719278.14, 17312.0, 307854.8, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0420 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,-0.0068, 302331.2,-0.0028, 302331.2,1.1933\n",
      "loop33.0, tr1361,wedgePops654620.06, 17312.0, 243196.8, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0420 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,-0.0981, 302331.2,-0.0028, 302331.2,1.1933\n",
      "loop34.0, tr1361,wedgePops717955.28, 17312.0, 306532.0, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0520 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,0.072, 302331.2,-0.0028, 302331.2,1.1933\n",
      "loop35.0, tr1361,wedgePops717738.48, 17312.0, 306315.2, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0620 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,-0.0037, 302331.2,-0.0028, 302331.2,1.1933\n",
      "loop36.0, tr1361,wedgePops669439.71, 17312.0, 258016.4, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0620 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,-0.0562, 302331.2,-0.0028, 302331.2,1.1933\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1361 1 258016.41654886553 1.6003\n",
      "loop37.0, tr1361,wedgePops720046.1, 17312.0, 308622.8, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0720 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,1.6932, 302331.2,-0.0028, 302331.2,1.1933\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1361 1 308622.8073338876 3.2935\n",
      "loop38.0, tr1361,wedgePops720046.1, 17312.0, 308622.8, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0820 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,-0.8466, 302331.2,-0.0028, 302331.2,1.1933\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 38.0 1 38.5 2.4469 308622.8\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1361 1 308622.8073338876 2.4469\n",
      "loop39.0, tr1361,wedgePops720046.1, 17312.0, 308622.8, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0820 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,-0.4233, 302331.2,-0.0028, 302331.2,1.1933\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.7881 7.063 17312.0486 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.0236 308622.8073 308622.8073 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.10531 303707.0858 302778.2962 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 3.61688 91313.6953 91332.948 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1361 39.0 1 38.5 2.0236 308622.8\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1361 1 308622.8073338876 2.0236\n",
      "loop40.0, tr1361,wedgePops720046.1, 17312.0, 308622.8, 302778.3, 91332.9, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0820 \n",
      "   targetWP, latest drx4 are tWP,dr, 302331.2,0.7819, 302331.2,-0.2117, 302331.2,-0.0028, 302331.2,1.1933\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.7881 7.063 17312.0486 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.81195 308622.8073 308622.8073 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.10531 303707.0858 302778.2962 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 3.61688 91313.6953 91332.948 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1361, giving up w pop 720046.1001063846\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 2.0 0 90.0 1.2934 327878.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1377 5.0 1 36.4 3.0182 396903.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1377 6.0 1 36.4 2.6478 396903.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1379 6.0 1 36.9 2.9626 402746.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1379 11.0 1 36.9 2.7618 402746.9\n",
      "I am working on tract number 1380 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1380 4.0 1 36.1 3.1709 368662.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1380 5.0 1 36.1 2.9309 368662.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1380 6.0 1 36.1 2.7091 368662.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1380 7.0 1 36.1 2.504 368662.9\n",
      "we have 2 non-opposing shorted wedges for tract no 1384\n",
      "we have 2 non-opposing shorted wedges for tract no 1392\n",
      "I am working on tract number 1400 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1409 1 202747.3300926154 2.8392\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1409 1 112154.55769008737 1.4196\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1409 1 202968.02321381995 2.8557\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1409 1 135632.12362678227 2.1377\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1409 1 148790.8914624853 2.4967\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1409 1 152907.5949888219 2.6762\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1409 1 158524.13560773936 2.766\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1409 1 182715.80142004485 2.8108\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1409 1 202663.4662899205 2.8333\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1409 1 200843.30563111758 2.8221\n",
      "I am working on tract number 1420 of 4706 tracts\n",
      "I am working on tract number 1440 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1440\n",
      "we have 2 non-opposing shorted wedges for tract no 1441\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1459 2.0 3 90.0 1.105 400263.5\n",
      "I am working on tract number 1460 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1469 5.0 1 36.3 3.4632 537582.1\n",
      "I am working on tract number 1480 of 4706 tracts\n",
      "I am working on tract number 1500 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1518\n",
      "I am working on tract number 1520 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1520\n",
      "we have 2 non-opposing shorted wedges for tract no 1527\n",
      "I am working on tract number 1540 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1544\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1545 1 432017.7976222866 6.4513\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1545 1 422505.4251871465 3.2256\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1545 1 285130.06259013095 1.6128\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1545 1 305260.44493247947 2.7341\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1545 1 424843.0615481274 3.2947\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1545 1 327393.86145455757 3.0144\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1545 1 418519.8590483195 3.1545\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1545 1 402737.0177948224 3.0845\n",
      "we have 2 non-opposing shorted wedges for tract no 1546\n",
      "we have 2 non-opposing shorted wedges for tract no 1548\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1549 3.0 1 37.6 1.5732 258419.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1551 8.0 1 41.0 2.2165 373305.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1555 2 204607.4491490125 2.6439\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1555 2 80943.91848306633 1.3219\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1555 2 197204.28259625216 2.5711\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1555 2 116301.71696537068 1.9465\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1555 2 139994.4808347019 2.2588\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1555 2 192192.14531799237 2.415\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1555 2 161811.92000833678 2.3369\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1555 2 188391.05755474285 2.3759\n",
      "I am working on tract number 1560 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1579\n",
      "I am working on tract number 1580 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1589 3.0 2 90.0 2.1298 224640.4\n",
      "we have 2 non-opposing shorted wedges for tract no 1590\n",
      "we have 2 non-opposing shorted wedges for tract no 1591\n",
      "I am working on tract number 1600 of 4706 tracts\n",
      "I am working on tract number 1620 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1626 3 193154.0901173473 2.6559\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1626 3 70820.73610800039 1.3279\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1626 3 193124.18936918964 2.6545\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1626 3 145284.35127477226 1.9912\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1626 3 164893.9987566417 2.3229\n",
      "I am working on tract number 1640 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 5.0 2 90.0 3.7743 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 6.0 2 90.0 3.7177 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 7.0 2 90.0 3.6619 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 8.0 2 90.0 3.607 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 9.0 2 90.0 3.5529 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 10.0 2 90.0 3.4996 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 11.0 2 90.0 3.4472 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 12.0 2 90.0 3.3955 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 13.0 2 90.0 3.3446 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 14.0 2 90.0 3.2944 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 15.0 2 90.0 3.245 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 16.0 2 90.0 3.1963 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 17.0 2 90.0 3.1484 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 18.0 2 90.0 3.1012 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 19.0 2 90.0 3.0547 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 20.0 2 90.0 3.0089 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 21.0 2 90.0 2.9638 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 22.0 2 90.0 2.9193 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 23.0 2 90.0 2.8755 181946.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 28.0 2 90.0 2.8619 181946.6\n",
      "loop31.0, tr1650,wedgePops670324.15, 178465.6, 178603.7, 134797.2, 178457.7, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?3340 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0014, 178326.8,-0.0012, 178326.8,1.1029, 178326.8,0.068\n",
      "loop32.0, tr1650,wedgePops717473.59, 178465.6, 178603.7, 181946.6, 178457.7, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?3340 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0014, 178326.8,-0.0012, 178326.8,0.3546, 178326.8,0.068\n",
      "loop33.0, tr1650,wedgePops717473.59, 178465.6, 178603.7, 181946.6, 178457.7, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?3350 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0014, 178326.8,-0.0012, 178326.8,-0.0205, 178326.8,0.068\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1650 33.0 2 90.0 2.8465 181946.6\n",
      "loop34.0, tr1650,wedgePops717459.91, 178465.6, 178603.7, 181932.9, 178457.7, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?3350 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0014, 178326.8,-0.0012, 178326.8,-0.0427, 178326.8,0.068\n",
      "loop35.0, tr1650,wedgePops592016.69, 178465.6, 178603.7, 56489.7, 178457.7, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?3350 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0014, 178326.8,-0.0012, 178326.8,-1.4019, 178326.8,0.068\n",
      "loop36.0, tr1650,wedgePops670259.42, 178465.6, 178603.7, 134732.4, 178457.7, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?3360 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0014, 178326.8,-0.0012, 178326.8,1.0972, 178326.8,0.068\n",
      "loop37.0, tr1650,wedgePops717473.59, 178465.6, 178603.7, 181946.6, 178457.7, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?3360 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0014, 178326.8,-0.0012, 178326.8,0.3509, 178326.8,0.068\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1650 2 181946.6228592099 2.85\n",
      "loop38.0, tr1650,wedgePops17.18, 4.3, 4.3, 4.3, 4.3, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0209, 178326.8,0.0209, 178326.8,0.0209, 178326.8,0.0209\n",
      "loop39.0, tr1650,wedgePops806781.73, 215443.3, 62378.6, 303309.7, 225650.1, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.6355, 178326.8,1.6355, 178326.8,1.6355, 178326.8,1.6355\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.65637 4.2947 215443.309 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.65637 4.2947 62378.6036 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.65637 4.2947 303309.7153 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.65637 4.2947 225650.0986 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr1650,wedgePops681085.93, 213712.3, 149206.1, 181214.5, 136953.0, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?1011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.1195, 178326.8,0.9153, 178326.8,-0.309, 178326.8,-0.1471\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.53685 215443.309 213712.3494 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.57166 62378.6036 149206.102 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.34737 303309.7153 181214.5017 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.50927 225650.0986 136952.9804 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1650, giving up w pop 681085.9335716313\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1655 5.0 3 90.0 4.1648 187838.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1655 6.0 3 90.0 4.0046 187838.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1655 7.0 3 90.0 3.8505 187838.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1655 8.0 3 90.0 3.7024 187838.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1655 9.0 3 90.0 3.5599 187838.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1655 10.0 3 90.0 3.423 187838.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1655 11.0 3 90.0 3.2913 187838.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1655 12.0 3 90.0 3.1647 187838.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1657 2 241749.90164219646 1.9549\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1657 2 26006.941961129545 0.9774\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1657 2 241870.57858084977 2.0228\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1657 2 78427.15396879253 1.5001\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1657 2 239002.37694788323 1.7615\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1657 2 182256.83806395548 1.6308\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1657 2 101208.77603299059 1.5655\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1657 2 138911.2508990666 1.5981\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1657 2 166736.93181969193 1.6145\n",
      "I am working on tract number 1660 of 4706 tracts\n",
      "I am working on tract number 1680 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1687 12.0 3 90.0 3.7169 207073.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1687 3 206188.79936303137 3.4779\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1687 3 92052.60236356253 1.7389\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1687 3 207073.33386220672 3.7549\n",
      "I am working on tract number 1700 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1701\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1713 7.0 0 83.3 1.0658 254592.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1714 7.0 0 83.3 1.0559 259307.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1717 5.0 1 65.0 0.9147 256965.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1719 7.0 1 65.1 1.0415 238613.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1719 8.0 1 65.1 0.9213 238613.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1719 9.0 1 65.1 0.815 238613.4\n",
      "I am working on tract number 1720 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1720 6.0 1 64.0 1.3137 238172.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1720 7.0 1 64.0 1.1683 238172.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1720 8.0 1 64.0 1.0389 238172.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1720 9.0 1 64.0 0.9239 238172.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1720 10.0 1 64.0 0.8216 238172.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1720 1 235726.6524259162 0.7545\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1720 1 13459.719479116 0.3773\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1720 1 235729.05545418427 0.7546\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1720 1 55832.59739993766 0.5659\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1720 1 148048.57705870227 0.6602\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1720 1 221813.58624841616 0.7074\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1720 1 186530.4270694023 0.6838\n",
      "I am working on tract number 1740 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1741 1 224560.82875950038 2.8031\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1741 1 65591.39426679164 1.4015\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1741 1 224476.48450555312 2.7988\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1741 1 138216.4272395834 2.1002\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1741 1 159024.93968752134 2.4495\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1741 1 220719.05323456827 2.6242\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1741 1 180536.76980965747 2.5368\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1741 1 214056.07031460563 2.5805\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1741 1 196699.1299349529 2.5587\n",
      "loop31.0, tr1741,wedgePops709357.08, 142074.9, 186450.7, 190859.2, 189972.3, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01321 \n",
      "   targetWP, latest drx4 are tWP,dr, 190410.8,1.6355, 190410.8,-0.0109, 190410.8,0.0417, 190410.8,-0.0043\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1741 1 186450.73724134202 2.5478\n",
      "loop32.0, tr1741,wedgePops714291.04, 142074.9, 191384.7, 190859.2, 189972.3, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01421 \n",
      "   targetWP, latest drx4 are tWP,dr, 190410.8,1.6355, 190410.8,0.0055, 190410.8,0.0417, 190410.8,-0.0043\n",
      "I am working on tract number 1760 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1769 3.0 1 37.4 1.6146 248369.6\n",
      "we have 2 non-opposing shorted wedges for tract no 1776\n",
      "I am working on tract number 1780 of 4706 tracts\n",
      "I am working on tract number 1800 of 4706 tracts\n",
      "I am working on tract number 1820 of 4706 tracts\n",
      "I am working on tract number 1840 of 4706 tracts\n",
      "I am working on tract number 1860 of 4706 tracts\n",
      "I am working on tract number 1880 of 4706 tracts\n",
      "I am working on tract number 1900 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 8.0 1 37.0 3.8334 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 9.0 1 37.0 3.7527 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 10.0 1 37.0 3.6736 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 11.0 1 37.0 3.5963 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 12.0 1 37.0 3.5206 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 13.0 1 37.0 3.4464 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 14.0 1 37.0 3.3739 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 15.0 1 37.0 3.3028 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 16.0 1 37.0 3.2333 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 17.0 1 37.0 3.1652 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 18.0 1 37.0 3.0985 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 19.0 1 37.0 3.0333 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 20.0 1 37.0 2.9694 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 21.0 1 37.0 2.9069 210965.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1917 7.0 3 58.4 4.288 295696.7\n",
      "I am working on tract number 1920 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1922\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1925 3 235817.60838159473 2.5901\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1925 3 65144.11161229227 1.2951\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1925 3 235804.41898120812 2.5898\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1925 3 143042.26544887084 1.9424\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1925 3 167367.40074582916 2.2661\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1925 3 211569.99004556952 2.4279\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1925 3 172526.572485119 2.347\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1925 3 180206.02582338025 2.3875\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1925 3 195085.92993451108 2.4077\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1932 1 211248.32647995654 2.108\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1932 1 70143.81102708739 1.054\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1932 1 212447.40614138287 2.1197\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1932 1 89257.69965326799 1.5869\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1932 1 105608.39827737649 1.8533\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1932 1 126116.40233536295 1.9865\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1932 1 200156.41742734163 2.0531\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1932 1 148257.87278165962 2.0198\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1932 1 188234.59075597653 2.0365\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1932 1 168761.83341651302 2.0282\n",
      "I am working on tract number 1940 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1959 9.0 0 98.2 1.1604 180046.0\n",
      "I am working on tract number 1960 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1966 6.0 3 90.0 0.9367 199966.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1966 7.0 3 90.0 0.8585 199966.1\n",
      "we have 2 non-opposing shorted wedges for tract no 1973\n",
      "I am working on tract number 1980 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1997 8.0 1 90.0 2.2546 252173.7\n",
      "I am working on tract number 2000 of 4706 tracts\n",
      "I am working on tract number 2020 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2031 11.0 0 87.9 0.812 185296.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2031 12.0 0 87.9 0.7889 185296.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2031 13.0 0 87.9 0.7664 185296.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2031 14.0 0 87.9 0.7446 185296.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2031 15.0 0 87.9 0.7234 185296.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2031 16.0 0 87.9 0.7028 185296.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2031 17.0 0 87.9 0.6828 185296.5\n",
      "I am working on tract number 2040 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2049 7.0 3 90.0 2.2655 225806.5\n",
      "I am working on tract number 2060 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 15.0 0 94.4 2.2436 191818.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 16.0 0 94.4 2.1231 191818.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 21.0 0 94.4 2.5941 191818.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 22.0 0 94.4 2.4548 191818.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 23.0 0 94.4 2.3229 191818.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 24.0 0 94.4 2.1982 191818.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 25.0 0 94.4 2.0801 191818.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 30.0 0 94.4 2.7063 191818.9\n",
      "loop31.0, tr2071,wedgePops726394.38, 191818.9, 178090.8, 178236.4, 178248.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.1454, 178326.8,0.0011, 178326.8,0.0002, 178326.8,0.2361\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 31.0 0 94.4 2.5609 191818.9\n",
      "loop32.0, tr2071,wedgePops726394.38, 191818.9, 178090.8, 178236.4, 178248.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.1376, 178326.8,0.0011, 178326.8,0.0002, 178326.8,0.2361\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 32.0 0 94.4 2.4234 191818.9\n",
      "loop33.0, tr2071,wedgePops726394.38, 191818.9, 178090.8, 178236.4, 178248.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.1302, 178326.8,0.0011, 178326.8,0.0002, 178326.8,0.2361\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 33.0 0 94.4 2.2932 191818.9\n",
      "loop34.0, tr2071,wedgePops726394.38, 191818.9, 178090.8, 178236.4, 178248.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.1232, 178326.8,0.0011, 178326.8,0.0002, 178326.8,0.2361\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 34.0 0 94.4 2.17 191818.9\n",
      "loop35.0, tr2071,wedgePops726394.38, 191818.9, 178090.8, 178236.4, 178248.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.1166, 178326.8,0.0011, 178326.8,0.0002, 178326.8,0.2361\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2071 35.0 0 94.4 2.0535 191818.9\n",
      "loop36.0, tr2071,wedgePops725591.58, 191016.1, 178090.8, 178236.4, 178248.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.1103, 178326.8,0.0011, 178326.8,0.0002, 178326.8,0.2361\n",
      "loop37.0, tr2071,wedgePops693108.69, 158533.2, 178090.8, 178236.4, 178248.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.9716, 178326.8,0.0011, 178326.8,0.0002, 178326.8,0.2361\n",
      "loop38.0, tr2071,wedgePops695149.3, 160573.8, 178090.8, 178236.4, 178248.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.5299, 178326.8,0.0011, 178326.8,0.0002, 178326.8,0.2361\n",
      "loop39.0, tr2071,wedgePops726394.38, 191818.9, 178090.8, 178236.4, 178248.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.755, 178326.8,0.0011, 178326.8,0.0002, 178326.8,0.2361\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 3.25641 160573.79 191818.8707 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.21998 176941.0883 178090.8097 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.14908 177869.6317 178236.4263 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.53127 137826.5002 178248.2731 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 yoyos for tract,wedge,wedgePop,r= 2071 0 191818.87071593595 3.2564\n",
      "loop40.0, tr2071,wedgePops696925.79, 162350.3, 178090.8, 178236.4, 178248.3, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 183652.4,-1.6282, 183652.4,0.0011, 183652.4,0.0002, 183652.4,0.2361\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.6282 191818.8707 162350.2817 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.21998 176941.0883 178090.8097 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.14908 177869.6317 178236.4263 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.53127 137826.5002 178248.2731 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 2071, giving up w pop 696925.7907655184\n",
      "I am working on tract number 2080 of 4706 tracts\n",
      "I am working on tract number 2100 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2117\n",
      "I am working on tract number 2120 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2135 0 237655.95904698945 2.4526\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2135 0 40671.21924641082 1.2263\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2135 0 240917.84462979715 2.5077\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2135 0 90425.72952386859 1.867\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2135 0 125205.3990580952 2.1873\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2135 0 208083.16865843133 2.3475\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2135 0 145143.11357013593 2.2674\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2135 0 159643.11823409077 2.3075\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2135 0 173384.28265010388 2.3275\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2135 0 190073.81839678757 2.3375\n",
      "I am working on tract number 2140 of 4706 tracts\n",
      "I am working on tract number 2160 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2162 5.0 0 83.6 1.0311 232346.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2162 6.0 0 83.6 0.965 232346.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2162 7.0 0 83.6 0.9031 232346.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 3.0 1 66.2 1.5993 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 4.0 1 66.2 1.5195 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 5.0 1 66.2 1.4437 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 6.0 1 66.2 1.3716 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 7.0 1 66.2 1.3031 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 8.0 1 66.2 1.2381 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 9.0 1 66.2 1.1763 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 10.0 1 66.2 1.1176 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 11.0 1 66.2 1.0618 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 12.0 1 66.2 1.0088 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 13.0 1 66.2 0.9584 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 14.0 1 66.2 0.9106 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 15.0 1 66.2 0.8651 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 16.0 1 66.2 0.8219 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 17.0 1 66.2 0.7809 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2163 18.0 1 66.2 0.7419 220253.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 3.0 1 51.5 1.6002 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 4.0 1 51.5 1.5175 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 5.0 1 51.5 1.4391 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 6.0 1 51.5 1.3647 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 7.0 1 51.5 1.2942 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 8.0 1 51.5 1.2273 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 9.0 1 51.5 1.1639 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 10.0 1 51.5 1.1037 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 11.0 1 51.5 1.0467 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 12.0 1 51.5 0.9926 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 13.0 1 51.5 0.9413 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2164 14.0 1 51.5 0.8926 222608.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2165 3.0 0 85.3 1.3642 212284.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2165 4.0 0 85.3 1.1934 212284.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2165 5.0 0 85.3 1.044 212284.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2171 6.0 0 142.5 3.5648 229395.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2173 3 214028.99172372735 1.084\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2173 3 39776.28877653883 0.542\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2173 3 214871.99295021358 1.1222\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2173 3 125955.04887278403 0.8321\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2173 3 163219.68381479793 0.9771\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2173 3 209287.13239192258 1.0496\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2173 3 165523.76051845073 1.0134\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2173 3 182739.05892877927 1.0315\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2173 3 167823.9154372968 1.0224\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2173 3 173536.12824519552 1.027\n",
      "I am working on tract number 2180 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2182 1 238073.02206669666 3.2258\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2182 1 43170.26834105048 1.6129\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2182 1 237801.75768648973 2.9658\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2182 3 276331.8935371679 1.163\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2182 1 163662.99858688097 2.2894\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2182 3 59041.683427435 0.5815\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2182 1 229711.23500848378 2.6276\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2182 3 276350.3028548134 1.1631\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2182 1 175878.16349815775 2.4585\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2182 3 108737.0211793062 0.8723\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2182 1 194740.43799044203 2.543\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2182 3 224730.10693682777 1.0177\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2182 1 227600.25062582028 2.5853\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2182 3 128878.96010191138 0.945\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2182 1 224514.19922172546 2.5642\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2182 3 153792.2862664347 0.9814\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2192 6.0 2 90.0 2.7019 179426.7\n",
      "I am working on tract number 2200 of 4706 tracts\n",
      "I am working on tract number 2220 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2220 2.0 1 90.0 1.5557 204644.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2220 2.0 2 90.0 1.4781 233200.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2220 3.0 1 90.0 1.4005 204644.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2220 3.0 2 90.0 1.1998 233200.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2220 4.0 1 90.0 1.2607 204644.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2220 4.0 2 90.0 0.9738 233200.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2222 1 291929.1836956056 2.7524\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2222 1 81519.44421941042 1.3762\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2222 1 239981.96965119676 2.5619\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2222 1 111425.73199105181 1.9691\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2222 1 226377.20689226515 2.2655\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2222 1 154875.43204159816 2.1173\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2222 1 219683.9568758143 2.1914\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2222 1 208642.87603310903 2.1543\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2222 1 181906.0625365518 2.1358\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2222 1 164093.5951420717 2.1266\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2222 1 172359.3659351473 2.1312\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2225 3 295050.1589306372 2.7809\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2225 3 77299.8409291722 1.3905\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2225 3 295056.25105122844 2.7812\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2225 3 161908.26170163596 2.0858\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2225 3 224832.13280756964 2.4335\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2225 3 217494.296716172 2.2597\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2225 3 212279.21178195163 2.1728\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2225 3 206165.1580418434 2.1293\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2225 3 197276.21841317185 2.1076\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2225 3 183035.6601420868 2.0967\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2225 3 172790.80504755361 2.0913\n",
      "loop31.0, tr2225,wedgePops712334.79, 177921.4, 178161.7, 178031.9, 178219.9, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?02212 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0034, 178326.8,0.0003, 178326.8,0.0043, 178326.8,0.0027\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2229 2.0 1 90.0 1.2295 290129.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2229 6.0 1 80.2 1.2376 312090.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 2.0 1 90.0 1.0014 316335.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 5.0 0 84.1 1.2306 186940.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 6.0 0 84.1 1.1876 186940.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 7.0 0 84.1 1.1461 186940.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 8.0 0 84.1 1.106 186940.2\n",
      "I am working on tract number 2240 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2245 1 213029.51730381217 2.1786\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2245 1 23870.444268303545 1.0893\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2245 1 212754.29615645448 2.1756\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2245 1 74673.53952681355 1.6324\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2245 1 96995.29805427128 1.904\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2245 1 128102.83485888346 2.0398\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2245 1 188407.37315695305 2.1077\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2245 1 142249.9160428521 2.0737\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2245 1 158914.68498311346 2.0907\n",
      "I am working on tract number 2260 of 4706 tracts\n",
      "I am working on tract number 2280 of 4706 tracts\n",
      "I am working on tract number 2300 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2312 11.0 3 95.4 3.5097 194419.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2313 1 209852.25808560412 2.0805\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2313 1 67370.39175201306 1.0402\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2313 1 209674.59820054317 2.0782\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2313 1 120985.88532008631 1.5592\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2313 1 143201.43916103218 1.8187\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2313 1 185139.9311337865 1.9485\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2313 1 152957.4930255338 1.8836\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2313 1 166754.2378276136 1.916\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2313 1 174340.0176384714 1.9323\n",
      "I am working on tract number 2320 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2320 6.0 1 90.0 3.7508 209849.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2320 11.0 1 90.0 3.7319 209849.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2320 16.0 1 90.0 3.7285 209849.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2320 1 209849.57523254748 4.0538\n",
      "we have 2 non-opposing shorted wedges for tract no 2331\n",
      "we have 2 non-opposing shorted wedges for tract no 2335\n",
      "I am working on tract number 2340 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2345\n",
      "we have 2 non-opposing shorted wedges for tract no 2346\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2348 8.0 1 90.0 3.6709 205813.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2348 14.0 0 86.5 3.6635 196404.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2349 2 185021.6783224162 2.2341\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2349 2 57387.52947404698 1.1171\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2349 2 187120.20664066676 2.2529\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2349 2 109139.63730130278 1.685\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2349 2 123462.85191303675 1.9689\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2349 2 137328.78207566772 2.1109\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2349 2 154132.17322032206 2.1819\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2356 2 196910.18134799524 2.273\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2356 2 64503.98959288871 1.1365\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2356 2 197508.49595521347 2.305\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2356 2 118711.85108992577 1.7208\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2356 2 140567.75639341617 2.0129\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2356 2 193085.72048676157 2.159\n",
      "I am working on tract number 2360 of 4706 tracts\n",
      "I am working on tract number 2380 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2388 1 202494.5346745046 1.8849\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2388 1 75272.48257989538 0.9424\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2388 1 207408.5557006789 1.894\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2388 1 94705.43877430257 1.4182\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2388 1 109865.72357476177 1.6561\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2388 1 120131.62670104568 1.775\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2388 1 136309.52916293408 1.8345\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2388 1 156056.2917921314 1.8642\n",
      "loop31.0, tr2388,wedgePops726612.49, 178509.4, 191268.1, 178536.3, 178298.7, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?11011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0005, 178326.8,0.0149, 178326.8,-0.0021, 178326.8,-0.0091\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2388 1 191268.12921105407 1.8791\n",
      "loop32.0, tr2388,wedgePops707892.78, 178509.4, 172548.4, 178536.3, 178298.7, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?11111 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0005, 178326.8,-0.0074, 178326.8,-0.0021, 178326.8,-0.0091\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2388 1 172548.41590335278 1.8717\n",
      "loop33.0, tr2388,wedgePops718157.02, 178509.4, 182812.7, 178536.3, 178298.7, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?11211 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0005, 178326.8,0.0037, 178326.8,-0.0021, 178326.8,-0.0091\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2388 1 182812.6542210547 1.8754\n",
      "loop34.0, tr2388,wedgePops713352.42, 178509.4, 178008.1, 178536.3, 178298.7, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?11311 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0005, 178326.8,-0.0019, 178326.8,-0.0021, 178326.8,-0.0091\n",
      "I am working on tract number 2400 of 4706 tracts\n",
      "I am working on tract number 2420 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2438 1 221120.90755158808 2.3417\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2438 1 63181.380117611 1.1708\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2438 1 231746.45610975838 2.4085\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2438 1 136388.33931820694 1.7897\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2438 1 205294.55252496287 2.0991\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2438 1 200832.93434677046 1.9444\n",
      "loop31.0, tr2438,wedgePops730536.67, 178460.3, 195095.8, 178547.7, 178432.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?11113 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0004, 178326.8,-0.0774, 178326.8,-0.0006, 178326.8,-0.0001\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2438 1 195095.84419473523 1.867\n",
      "loop32.0, tr2438,wedgePops722680.41, 178460.3, 187239.6, 178547.7, 178432.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?11113 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0004, 178326.8,-0.0387, 178326.8,-0.0006, 178326.8,-0.0001\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2438 1 187239.58691867307 1.8283\n",
      "loop33.0, tr2438,wedgePops715327.3, 178460.3, 179886.5, 178547.7, 178432.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?11113 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0004, 178326.8,-0.0193, 178326.8,-0.0006, 178326.8,-0.0001\n",
      "I am working on tract number 2440 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2444 6.0 3 90.0 2.4178 231512.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2444 11.0 3 90.0 2.2886 231512.3\n",
      "we have 2 non-opposing shorted wedges for tract no 2456\n",
      "I am working on tract number 2460 of 4706 tracts\n",
      "I am working on tract number 2480 of 4706 tracts\n",
      "I am working on tract number 2500 of 4706 tracts\n",
      "I am working on tract number 2520 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2539 4.0 3 90.0 2.3505 209086.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2539 9.0 3 90.0 2.5022 209086.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2539 3 209086.09135746455 2.3773\n",
      "I am working on tract number 2540 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2549 1 181962.89316015661 1.8129\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2549 1 53435.22508950163 0.9064\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2549 1 193920.64658377843 1.8617\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2549 1 72415.86841796972 1.3841\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2549 1 87610.56704842002 1.6229\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2549 1 105882.28990154699 1.7423\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2549 1 173140.25416268627 1.802\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2549 1 188208.20042316464 1.8318\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2549 1 183341.92081065816 1.8169\n",
      "I am working on tract number 2560 of 4706 tracts\n",
      "I am working on tract number 2580 of 4706 tracts\n",
      "I am working on tract number 2600 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2619 6.0 1 52.7 1.3451 304914.8\n",
      "I am working on tract number 2620 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2621 6.0 1 54.4 1.2427 313043.6\n",
      "we have 2 non-opposing shorted wedges for tract no 2633\n",
      "I am working on tract number 2640 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 5.0 1 47.9 1.9563 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 6.0 1 47.9 1.8491 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 7.0 1 47.9 1.7477 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 8.0 1 47.9 1.683 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 9.0 1 47.9 1.6206 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 10.0 1 47.9 1.5606 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 11.0 1 47.9 1.5028 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 12.0 1 47.9 1.4471 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 13.0 1 47.9 1.3935 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 14.0 1 47.9 1.3418 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 15.0 1 47.9 1.2921 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 16.0 1 47.9 1.2443 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 17.0 1 47.9 1.1982 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 18.0 1 47.9 1.1538 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 19.0 1 47.9 1.111 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2640 20.0 1 47.9 1.0699 238560.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2641 5.0 1 54.6 1.2496 401873.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2642 3.0 1 51.0 1.4961 266618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2642 4.0 1 51.0 1.2285 266618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2642 5.0 1 51.0 1.0087 266618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2643 3.0 1 61.7 1.317 353993.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2645 3.0 1 50.3 1.4664 286546.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2645 4.0 1 50.3 1.1548 286546.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2647 6.0 1 53.1 1.1423 283131.7\n",
      "we have 2 non-opposing shorted wedges for tract no 2651\n",
      "we have 2 non-opposing shorted wedges for tract no 2654\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2659 5.0 0 83.5 1.2847 226805.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2659 6.0 0 83.5 1.0664 226805.2\n",
      "I am working on tract number 2660 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2661 6.0 1 68.0 1.5368 226659.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2661 7.0 1 68.0 1.415 226659.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2661 8.0 1 68.0 1.3028 226659.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2661 9.0 1 68.0 1.1995 226659.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2661 10.0 1 68.0 1.1044 226659.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2661 11.0 1 68.0 1.0169 226659.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2661 12.0 1 68.0 0.9363 226659.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2661 13.0 1 68.0 0.8621 226659.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2661 14.0 1 68.0 0.7937 226659.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2662 6.0 0 83.4 1.0357 252721.7\n",
      "we have 2 non-opposing shorted wedges for tract no 2664\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2665 3.0 1 43.3 1.4511 292290.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2666 10.0 1 54.0 1.2985 318718.3\n",
      "we have 2 non-opposing shorted wedges for tract no 2671\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2673 7.0 3 46.7 3.7102 321850.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2673 8.0 3 46.7 3.6641 321850.0\n",
      "we have 2 non-opposing shorted wedges for tract no 2677\n",
      "I am working on tract number 2680 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2683 3.0 0 111.5 1.6169 190607.8\n",
      "we have 2 non-opposing shorted wedges for tract no 2693\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2696 0 207447.50209626203 3.1378\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2698 2.0 3 90.0 1.3734 282133.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2698 3.0 3 90.0 0.9512 282133.8\n",
      "I am working on tract number 2700 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2701\n",
      "we have 2 non-opposing shorted wedges for tract no 2702\n",
      "we have 2 non-opposing shorted wedges for tract no 2704\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2706 0 190091.3276409278 2.7084\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2706 0 70253.96218409369 1.3542\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2706 0 205229.73839566016 2.7478\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2706 0 128725.88073076036 2.051\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2706 0 148289.7768233351 2.3994\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2706 0 155520.51483127277 2.5736\n",
      "loop31.0, tr2706,wedgePops700305.83, 168592.2, 167523.5, 182304.9, 181885.2, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?10031 \n",
      "   targetWP, latest drx4 are tWP,dr, 181928.0,0.0871, 181928.0,0.3832, 181928.0,-0.004, 181928.0,-0.0063\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2706 0 168592.20380426323 2.6607\n",
      "loop32.0, tr2706,wedgePops718809.16, 187095.5, 167523.5, 182304.9, 181885.2, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?10031 \n",
      "   targetWP, latest drx4 are tWP,dr, 181928.0,0.0435, 181928.0,0.3832, 181928.0,-0.004, 181928.0,-0.0063\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2706 0 187095.53473097694 2.7042\n",
      "loop33.0, tr2706,wedgePops705565.82, 173852.2, 167523.5, 182304.9, 181885.2, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?11031 \n",
      "   targetWP, latest drx4 are tWP,dr, 181928.0,-0.0218, 181928.0,0.3832, 181928.0,-0.004, 181928.0,-0.0063\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2706 0 173852.18964656582 2.6825\n",
      "loop34.0, tr2706,wedgePops711309.28, 179595.7, 167523.5, 182304.9, 181885.2, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?12031 \n",
      "   targetWP, latest drx4 are tWP,dr, 181928.0,0.0109, 181928.0,0.3832, 181928.0,-0.004, 181928.0,-0.0063\n",
      "I am working on tract number 2720 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2728 0 256991.6601141747 2.6673\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2728 30.0 0 99.4 2.3203 245752.9\n",
      "loop31.0, tr2728,wedgePops687257.74, 151722.0, 178766.3, 178084.4, 178685.1, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 187195.1,-0.5157, 187195.1,-0.0019, 187195.1,0.0107, 187195.1,-0.0031\n",
      "loop32.0, tr2728,wedgePops708555.11, 151722.0, 185678.4, 184404.3, 186750.4, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 187195.1,-0.5157, 187195.1,0.0074, 187195.1,0.003, 187195.1,0.0296\n",
      "loop33.0, tr2728,wedgePops711873.74, 151722.0, 186829.7, 186571.7, 186750.4, Overedge?1, 0, 0, 0, ,Satisfied?0001,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 187195.1,-0.5157, 187195.1,0.0013, 187195.1,0.001, 187195.1,0.0296\n",
      "we have 2 non-opposing shorted wedges for tract no 2738\n",
      "I am working on tract number 2740 of 4706 tracts\n",
      "I am working on tract number 2760 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2769 10.0 1 82.5 1.0702 334719.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2775 8.0 1 80.4 1.0264 330977.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2778 8.0 1 81.1 1.2916 340053.5\n",
      "I am working on tract number 2780 of 4706 tracts\n",
      "I am working on tract number 2800 of 4706 tracts\n",
      "I am working on tract number 2820 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2829\n",
      "I am working on tract number 2840 of 4706 tracts\n",
      "I am working on tract number 2860 of 4706 tracts\n",
      "I am working on tract number 2880 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 6.0 2 90.0 2.8839 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 7.0 2 90.0 2.7678 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 8.0 2 90.0 2.6564 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 9.0 2 90.0 2.5494 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 10.0 2 90.0 2.4468 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 11.0 2 90.0 2.3483 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 12.0 2 90.0 2.2538 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 13.0 2 90.0 2.163 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 14.0 2 90.0 2.0759 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 15.0 2 90.0 1.9924 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 20.0 2 90.0 3.1862 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 21.0 2 90.0 3.0579 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 22.0 2 90.0 2.9348 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 23.0 2 90.0 2.8167 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 24.0 2 90.0 2.7033 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 25.0 2 90.0 2.5945 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 26.0 2 90.0 2.49 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 27.0 2 90.0 2.3898 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 28.0 2 90.0 2.2936 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 29.0 2 90.0 2.2012 188298.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 30.0 2 90.0 2.1126 188298.5\n",
      "loop31.0, tr2898,wedgePops723094.13, 178450.8, 178210.2, 188298.5, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1030 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,-0.085, 178326.8,0.0007\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 31.0 2 90.0 2.0276 188298.5\n",
      "loop32.0, tr2898,wedgePops723032.82, 178450.8, 178210.2, 188237.2, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1030 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,-0.0816, 178326.8,0.0007\n",
      "loop33.0, tr2898,wedgePops624709.06, 178450.8, 178210.2, 89913.4, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1030 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,-0.973, 178326.8,0.0007\n",
      "loop34.0, tr2898,wedgePops638674.12, 178450.8, 178210.2, 103878.5, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1040 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,0.7184, 178326.8,0.0007\n",
      "loop35.0, tr2898,wedgePops723094.13, 178450.8, 178210.2, 188298.5, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1040 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,1.5385, 178326.8,0.0007\n",
      "loop36.0, tr2898,wedgePops723094.13, 178450.8, 178210.2, 188298.5, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1050 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,-0.1132, 178326.8,0.0007\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 36.0 2 90.0 3.1166 188298.5\n",
      "loop37.0, tr2898,wedgePops723094.13, 178450.8, 178210.2, 188298.5, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1050 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,-0.1255, 178326.8,0.0007\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 37.0 2 90.0 2.9911 188298.5\n",
      "loop38.0, tr2898,wedgePops723094.13, 178450.8, 178210.2, 188298.5, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1050 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,-0.1204, 178326.8,0.0007\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 38.0 2 90.0 2.8707 188298.5\n",
      "loop39.0, tr2898,wedgePops723094.13, 178450.8, 178210.2, 188298.5, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1050 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,-0.1156, 178326.8,0.0007\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.18534 178972.9647 178450.8428 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.21948 177598.0326 178210.2354 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.75514 188298.4706 188298.4706 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.32735 177380.2044 178134.5802 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2898 39.0 2 90.0 2.7551 188298.5\n",
      "loop40.0, tr2898,wedgePops723094.13, 178450.8, 178210.2, 188298.5, 178134.6, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?1050 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0002, 178326.8,0.0006, 178326.8,-0.1109, 178326.8,0.0007\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.18534 178972.9647 178450.8428 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.21948 177598.0326 178210.2354 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.64422 188298.4706 188298.4706 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.32735 177380.2044 178134.5802 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 2898, giving up w pop 723094.1289477645\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2899 2 243987.63324091234 2.8544\n",
      "I am working on tract number 2900 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2904 7.0 0 90.0 0.9117 199911.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2904 8.0 0 90.0 0.8357 199911.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2904 0 199911.8858135687 0.8554\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_103/99954720.py:66: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
      "/tmp/ipykernel_103/99954720.py:99: RuntimeWarning: invalid value encountered in multiply\n",
      "  wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 2920 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2930 14.0 2 90.0 2.6484 228789.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2930 15.0 2 90.0 2.1831 228789.0\n",
      "I am working on tract number 2940 of 4706 tracts\n",
      "I am working on tract number 2960 of 4706 tracts\n",
      "I am working on tract number 2980 of 4706 tracts\n",
      "I am working on tract number 3000 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3001 3.0 3 74.0 1.3987 250983.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3001 4.0 3 74.0 1.1544 250983.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3006 2.0 1 90.0 1.45 245021.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3006 3.0 1 90.0 1.1305 245021.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3015 3.0 0 85.8 1.5551 210789.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3015 4.0 0 85.8 1.399 210789.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3015 5.0 0 85.8 1.2585 210789.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3015 6.0 0 85.8 1.1322 210789.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3015 7.0 0 85.8 1.0185 210789.3\n",
      "I am working on tract number 3020 of 4706 tracts\n",
      "I am working on tract number 3040 of 4706 tracts\n",
      "I am working on tract number 3060 of 4706 tracts\n",
      "I am working on tract number 3080 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3086\n",
      "we have 2 non-opposing shorted wedges for tract no 3091\n",
      "we have 2 non-opposing shorted wedges for tract no 3097\n",
      "we have 2 non-opposing shorted wedges for tract no 3098\n",
      "I am working on tract number 3100 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3115\n",
      "I am working on tract number 3120 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3138 1 240114.47783846344 2.1498\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3138 1 80343.35590094497 1.0749\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3138 1 240131.14285551853 2.1502\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3138 1 117149.09926543864 1.6125\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3138 1 223839.3792035757 1.8814\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3138 1 140722.57331368292 1.747\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3138 1 211344.51056575959 1.8142\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3138 1 170531.36783106386 1.7806\n",
      "15 yoyos for tract,wedge,wedgePop,r= 3138 1 205246.32675420312 1.7974\n",
      "15 yoyos for tract,wedge,wedgePop,r= 3138 1 190982.39055930954 1.789\n",
      "I am working on tract number 3140 of 4706 tracts\n",
      "I am working on tract number 3160 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3164\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3177 6.0 1 90.0 2.4786 209523.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3177 11.0 1 90.0 2.5732 209523.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3177 1 209523.02564023208 2.4133\n",
      "I am working on tract number 3180 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 3.0 1 90.0 1.6864 184439.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 4.0 1 90.0 1.6442 184439.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 5.0 1 90.0 1.603 184439.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 9.0 1 62.6 1.3462 226510.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 10.0 1 62.6 1.2487 226510.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 11.0 1 62.6 1.1583 226510.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 12.0 1 62.6 1.0745 226510.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 13.0 1 62.6 0.9967 226510.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 14.0 1 62.6 0.9245 226510.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 15.0 1 62.6 0.8576 226510.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3186 16.0 1 62.6 0.7955 226510.7\n",
      "we have 2 non-opposing shorted wedges for tract no 3187\n",
      "we have 2 non-opposing shorted wedges for tract no 3194\n",
      "I am working on tract number 3200 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3200\n",
      "we have 2 non-opposing shorted wedges for tract no 3210\n",
      "I am working on tract number 3220 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3226 0 248115.45609987364 2.4595\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3226 0 62136.452846764034 1.2298\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3226 0 255794.79116483981 2.5897\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3226 0 99386.31054538616 1.9097\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3226 0 237571.05759936545 2.2497\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3226 0 143408.5770944023 2.0797\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3226 0 220736.69889219833 2.1647\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3226 0 163176.1892094764 2.1222\n",
      "15 yoyos for tract,wedge,wedgePop,r= 3226 0 188431.04420698175 2.1434\n",
      "16 yoyos for tract,wedge,wedgePop,r= 3226 0 170076.4450181425 2.1328\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3232 9.0 0 125.7 2.3552 196454.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3232 14.0 0 125.7 2.3577 196454.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3232 0 196454.4132921854 2.4462\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3234 1 185369.4667534526 2.2194\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3234 1 32053.051150947227 1.1097\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3234 1 183976.2050893677 2.1959\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3234 1 80582.63977721942 1.6528\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3234 1 112858.5685279477 1.9244\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3234 1 135041.51717302867 2.0601\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3234 1 147573.92885923383 2.128\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3234 1 182845.97570224066 2.162\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3234 1 166972.03414192543 2.145\n",
      "I am working on tract number 3240 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 5.0 0 90.0 2.5972 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 6.0 0 90.0 2.387 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 7.0 0 90.0 2.1938 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 8.0 0 90.0 2.0163 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 13.0 0 90.0 2.9442 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 14.0 0 90.0 2.706 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 15.0 0 90.0 2.487 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 16.0 0 90.0 2.2857 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 17.0 0 90.0 2.1007 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 18.0 0 90.0 1.9307 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 23.0 0 90.0 3.0195 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 24.0 0 90.0 2.7752 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 25.0 0 90.0 2.5506 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 26.0 0 90.0 2.3442 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 27.0 0 90.0 2.1545 199246.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 28.0 0 90.0 1.9801 199246.0\n",
      "loop31.0, tr3247,wedgePops642788.13, 107638.0, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.6099, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "loop32.0, tr3247,wedgePops734396.07, 199246.0, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.9475, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3247 0 199245.98041014 3.4673\n",
      "loop33.0, tr3247,wedgePops730440.75, 195290.7, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?7012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-1.7337, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3247 0 195290.66601048145 1.7337\n",
      "loop34.0, tr3247,wedgePops634723.1, 99573.0, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?7012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.8668, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3247 0 99573.01527538778 0.8668\n",
      "loop35.0, tr3247,wedgePops684368.05, 149218.0, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?8012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.8159, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3247 0 149217.96409879485 1.6827\n",
      "loop36.0, tr3247,wedgePops734396.07, 199246.0, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?8012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.408, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3247 0 199245.98041014007 2.0907\n",
      "loop37.0, tr3247,wedgePops734396.07, 199246.0, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?9012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.204, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3247 37.0 0 90.0 1.8867 199246.0\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3247 0 199245.98041014007 1.8867\n",
      "loop38.0, tr3247,wedgePops732974.93, 197824.8, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?9012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.102, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3247 0 197824.8469848272 1.7847\n",
      "loop39.0, tr3247,wedgePops730446.86, 195296.8, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?9012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.051, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.73374 197824.847 195296.7738 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.34847 175241.0098 178595.6638 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.24136 179100.0522 178452.2314 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.2647 177228.4609 178102.1899 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 yoyos for tract,wedge,wedgePop,r= 3247 0 195296.77375668514 1.7337\n",
      "loop40.0, tr3247,wedgePops719237.36, 184087.3, 178595.7, 178452.2, 178102.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?9012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0255, 178326.8,0.0328, 178326.8,-0.0004, 178326.8,0.0004\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.70825 195296.7738 184087.2773 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.34847 175241.0098 178595.6638 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.24136 179100.0522 178452.2314 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.2647 177228.4609 178102.1899 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 3247, giving up w pop 719237.3624981609\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3255 8.0 2 90.0 3.7302 201697.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3255 20.0 0 87.4 4.2018 191886.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3255 21.0 0 87.4 3.975 191886.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3255 22.0 0 87.4 3.7605 191886.9\n",
      "I am working on tract number 3260 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3270\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3275 5.0 1 90.0 3.5688 203478.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3275 11.0 0 87.3 3.7015 192609.4\n",
      "I am working on tract number 3280 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3282 0 288127.5788061086 2.7884\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3288 0 31392.832162480598 1.0542\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3288 0 288394.54393738066 3.3105\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3288 0 287410.46415295097 2.1824\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3288 0 57809.264634635765 1.6183\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3288 0 181497.49882613448 1.9004\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3288 0 212688.58916131093 2.0414\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3288 0 282192.33713112236 2.1119\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3288 0 244597.49189461596 2.0766\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3292 7.0 0 83.7 2.3041 276944.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3298 12.0 0 104.7 3.4978 202893.3\n",
      "I am working on tract number 3300 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3303 3 203905.55415980925 2.5782\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3303 3 63007.22064396701 1.2891\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3303 3 201550.44767626608 2.546\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3303 3 113775.23753645139 1.9176\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3303 3 131218.71617307886 2.2318\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3303 3 152088.81498078367 2.3889\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3303 3 194684.44532339956 2.4674\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3304 9.0 0 110.5 3.6806 187517.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3304 10.0 0 110.5 3.5436 187517.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3304 11.0 0 110.5 3.4117 187517.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3304 16.0 0 110.5 3.447 187517.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3304 3 296944.35226153943 2.616\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3304 3 31721.333328293927 1.308\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3304 3 296944.6752686672 2.6172\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3304 21.0 0 110.5 3.5025 187517.3\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3304 3 106280.66725352113 1.9626\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3304 22.0 0 110.5 3.3721 187517.3\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3304 3 276864.1973078593 2.2899\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3304 3 157875.3549469367 2.1263\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3304 3 216275.15017825534 2.2081\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3304 3 172250.205891193 2.1672\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3304 0 187517.27557092838 3.3402\n",
      "loop31.0, tr3304,wedgePops711544.42, 105467.8, 200620.8, 202966.0, 202489.8, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?0314 \n",
      "   targetWP, latest drx4 are tWP,dr, 202613.2,-1.6701, 202613.2,0.0089, 202613.2,0.0868, 202613.2,0.0003\n",
      "we have 2 non-opposing shorted wedges for tract no 3310\n",
      "I am working on tract number 3320 of 4706 tracts\n",
      "I am working on tract number 3340 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3347 2.0 3 90.0 1.4469 182696.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3347 3.0 3 90.0 1.4208 182696.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3347 4.0 3 90.0 1.3952 182696.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3347 5.0 3 90.0 1.37 182696.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3348 2 299755.8218275738 3.0858\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3348 2 93303.01241484232 1.5429\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3348 2 299980.5335967175 3.1225\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3348 2 155509.9938329658 2.3327\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3348 2 235703.00676630135 2.7276\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3348 2 233104.88077208828 2.5302\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3348 2 228172.74976930584 2.4314\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3348 2 218372.57063055394 2.3821\n",
      "we have 2 non-opposing shorted wedges for tract no 3352\n",
      "I am working on tract number 3360 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3361 8.0 1 37.9 3.6022 421571.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3361 13.0 1 37.9 3.3328 421571.5\n",
      "we have 2 non-opposing shorted wedges for tract no 3363\n",
      "we have 2 non-opposing shorted wedges for tract no 3364\n",
      "we have 2 non-opposing shorted wedges for tract no 3365\n",
      "we have 2 non-opposing shorted wedges for tract no 3367\n",
      "we have 2 non-opposing shorted wedges for tract no 3368\n",
      "we have 2 non-opposing shorted wedges for tract no 3370\n",
      "we have 2 non-opposing shorted wedges for tract no 3371\n",
      "I am working on tract number 3380 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3387 5.0 0 141.2 1.9545 183271.2\n",
      "I am working on tract number 3400 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3408\n",
      "we have 2 non-opposing shorted wedges for tract no 3410\n",
      "I am working on tract number 3420 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3422\n",
      "I am working on tract number 3440 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3452 1 279656.7316207628 2.8211\n",
      "I am working on tract number 3460 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3466\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3468 4.0 1 38.0 2.8624 504813.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3469 4.0 1 36.0 3.1118 404180.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3469 9.0 1 36.0 3.1354 404180.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3469 14.0 1 36.0 3.062 404180.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3469 1 404180.4722594916 2.8636\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 8.0 2 90.0 3.4407 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 9.0 2 90.0 3.3478 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 10.0 2 90.0 3.2573 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 11.0 2 90.0 3.1693 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 12.0 2 90.0 3.0837 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 17.0 2 90.0 3.5623 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 18.0 2 90.0 3.466 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 19.0 2 90.0 3.3724 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 20.0 2 90.0 3.2813 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 21.0 2 90.0 3.1926 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 22.0 2 90.0 3.1064 184928.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 23.0 2 90.0 3.0225 184928.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3475 2 184928.32162040952 3.57\n",
      "loop31.0, tr3475,wedgePops538365.43, 153578.7, 169294.8, 93420.1, 122071.9, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?1012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0661, 178326.8,0.0833, 178326.8,-0.2316, 178326.8,0.0914\n",
      "loop32.0, tr3475,wedgePops712335.77, 163508.9, 176696.5, 128073.4, 244057.0, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?2022 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0327, 178326.8,0.1149, 178326.8,0.1151, 178326.8,0.0974\n",
      "we have 2 non-opposing shorted wedges for tract no 3478\n",
      "I am working on tract number 3480 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3483\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3487 4.0 1 36.1 3.1489 388894.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3487 9.0 1 36.1 3.4954 388894.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3487 10.0 1 36.1 3.1523 388894.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3487 15.0 1 36.1 3.4949 388894.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3487 16.0 1 36.1 3.1519 388894.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3487 1 388894.0875827988 3.6473\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3488 5.0 1 36.3 3.1689 388154.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3488 6.0 1 36.3 2.833 388154.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3488 11.0 1 36.3 2.8569 388154.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3488 16.0 1 36.3 2.8617 388154.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3488 1 388154.8434224195 2.985\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3494 13.0 0 83.8 2.7473 199360.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3494 0 199360.8781105586 2.866\n",
      "I am working on tract number 3500 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3511 5.0 0 138.3 1.6482 214220.1\n",
      "I am working on tract number 3520 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3523 9.0 2 90.0 3.4675 199381.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3523 14.0 2 90.0 3.8491 199381.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3523 15.0 2 90.0 3.5358 199381.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3523 2 199381.4255413639 4.4154\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3538 5.0 0 90.0 1.7096 204926.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3538 6.0 0 90.0 1.5374 204926.6\n",
      "I am working on tract number 3540 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3541\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3542 2 189435.93279806562 3.1457\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3542 2 71441.69116049593 1.5729\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3542 2 189507.75672914568 3.1608\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3542 2 122052.7551442358 2.3668\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3542 2 136733.30291099715 2.7638\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3542 2 188024.76882810908 2.9623\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3542 2 155106.70961204605 2.863\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3542 2 185488.44682967226 2.9127\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3542 2 172084.13048514925 2.8879\n",
      "we have 2 non-opposing shorted wedges for tract no 3543\n",
      "we have 2 non-opposing shorted wedges for tract no 3547\n",
      "we have 2 non-opposing shorted wedges for tract no 3550\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3553 2 251923.91020485747 2.62\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3553 2 60782.370084858965 1.31\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3553 2 251501.32359223673 2.6144\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3553 2 121946.65445293953 1.9622\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3553 2 233853.90028861997 2.2883\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3553 2 213068.25847821895 2.1253\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3553 2 194197.7257790045 2.0437\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3553 2 169567.09644088987 2.003\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3553 2 187665.05508519858 2.0234\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3553 2 183156.68057313122 2.0132\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3558 1 249645.94822638616 2.6294\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3558 1 110720.39208060663 1.3147\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3558 1 249645.00833376145 2.6285\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3558 1 135103.00769815952 1.9716\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3558 1 246253.38848455803 2.3001\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3558 1 169263.55691692163 2.1358\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3558 1 240514.4105137026 2.218\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3558 1 205854.8694044122 2.1769\n",
      "I am working on tract number 3560 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3567 11.0 0 112.7 2.713 227315.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3567 16.0 0 112.7 2.8427 227315.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3567 17.0 0 112.7 2.3554 227315.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3570 11.0 0 114.3 2.6487 230869.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3571 14.0 0 113.3 2.7596 226288.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3571 0 191662.81493147757 2.2923\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3571 0 92009.24041792843 1.1462\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3571 0 219419.55929745687 2.3303\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3571 0 103923.67306734023 1.7382\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3571 0 117382.77602730929 2.0343\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3571 0 139984.45592037082 2.1823\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3571 0 156870.73598944017 2.2563\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3571 0 192832.6410693147 2.2933\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3571 0 168412.0748754659 2.2748\n",
      "loop31.0, tr3571,wedgePops715353.54, 181259.4, 178134.3, 178029.4, 177930.5, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?12001 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0093, 178326.8,0.0012, 178326.8,0.0008, 178326.8,-0.0046\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 6.0 2 90.0 2.4286 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 7.0 2 90.0 2.191 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 12.0 2 90.0 3.0191 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 13.0 2 90.0 2.7238 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 14.0 2 90.0 2.4574 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 15.0 2 90.0 2.217 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 20.0 2 90.0 3.3432 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 21.0 2 90.0 3.0161 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 22.0 2 90.0 2.7211 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 23.0 2 90.0 2.455 204074.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 24.0 2 90.0 2.2148 204074.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3574 2 204074.7681567159 4.2381\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 29.0 2 90.0 2.119 204074.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3574 2 204074.7681567159 2.119\n",
      "loop31.0, tr3574,wedgePops413348.96, 42758.7, 92066.4, 178758.0, 99765.7, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.1786, 178326.8,0.1786, 178326.8,0.1786, 178326.8,0.1786\n",
      "loop32.0, tr3574,wedgePops507105.39, 85865.5, 124423.8, 178758.0, 118058.1, Overedge?0, 0, 0, 0, ,Satisfied?0010,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.1504, 178326.8,0.0565, 178326.8,0.1786, 178326.8,0.0486\n",
      "loop33.0, tr3574,wedgePops582699.08, 104296.2, 170439.6, 178758.0, 129205.2, Overedge?0, 0, 0, 0, ,Satisfied?0010,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.1544, 178326.8,0.0576, 178326.8,0.1786, 178326.8,0.0916\n",
      "loop34.0, tr3574,wedgePops632645.06, 118562.1, 175233.7, 178758.0, 160091.2, Overedge?0, 0, 0, 0, ,Satisfied?0010,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.3007, 178326.8,0.007, 178326.8,0.1786, 178326.8,0.1994\n",
      "loop35.0, tr3574,wedgePops656719.35, 121870.7, 177297.7, 178758.0, 178792.9, Overedge?0, 0, 0, 0, ,Satisfied?0010,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.563, 178326.8,0.0036, 178326.8,0.1786, 178326.8,0.0702\n",
      "loop36.0, tr3574,wedgePops772617.23, 237275.5, 178150.3, 178758.0, 178433.4, Overedge?0, 0, 0, 0, ,Satisfied?0010,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.6355, 178326.8,0.0014, 178326.8,0.1786, 178326.8,-0.0013\n",
      "loop37.0, tr3574,wedgePops772617.23, 237275.5, 178150.3, 178758.0, 178433.4, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?1001 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.5481, 178326.8,0.0014, 178326.8,0.1786, 178326.8,-0.0013\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3574 37.0 0 109.4 2.4362 237275.5\n",
      "loop38.0, tr3574,wedgePops681415.41, 146073.7, 178150.3, 178758.0, 178433.4, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 189077.9,-0.4863, 189077.9,0.0014, 189077.9,0.1786, 189077.9,-0.0013\n",
      "loop39.0, tr3574,wedgePops709236.66, 146073.7, 188689.2, 186926.2, 187547.6, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 189077.9,-0.4863, 189077.9,0.014, 189077.9,0.0041, 189077.9,0.0303\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.94993 237275.5143 146073.6938 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.32049 178150.2986 188689.1957 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.1845 178758.0342 186926.1756 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.61924 178433.3799 187547.5928 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr3574,wedgePops712470.32, 146073.7, 188689.2, 188640.6, 189066.9, Overedge?1, 0, 0, 0, ,Satisfied?0100,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 189077.9,-0.4863, 189077.9,0.014, 189077.9,0.0009, 189077.9,0.004\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.94993 237275.5143 146073.6938 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.32049 178150.2986 188689.1957 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.18535 186926.1756 188640.559 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.62319 187547.5928 189066.8725 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 3574, giving up w pop 712470.3210088392\n",
      "we have 2 non-opposing shorted wedges for tract no 3577\n",
      "I am working on tract number 3580 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3588 6.0 0 127.9 3.0838 200165.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3588 0 200165.25660288622 3.1957\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3590 10.0 0 90.0 2.7558 230569.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3590 15.0 0 90.0 2.7949 230569.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3590 0 230569.17013156466 3.2178\n",
      "I am working on tract number 3600 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3602\n",
      "we have 2 non-opposing shorted wedges for tract no 3603\n",
      "we have 2 non-opposing shorted wedges for tract no 3605\n",
      "we have 2 non-opposing shorted wedges for tract no 3606\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3609 5.0 1 63.3 0.8555 235753.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3610 2 203556.786673552 2.9448\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3610 2 64382.028688416 1.4724\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3610 2 203238.85258359 2.9383\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3610 2 128611.6958352346 2.2054\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3610 2 146138.50681883845 2.5718\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3610 2 198190.45222759093 2.7551\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3610 2 189915.86044232338 2.6635\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3610 2 155517.68847942274 2.6177\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3610 2 166319.19841487255 2.6406\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3610 2 171887.48959183006 2.652\n",
      "I am working on tract number 3620 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3623 3.0 1 90.0 1.9802 179719.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3623 4.0 1 90.0 1.9687 179719.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3623 5.0 1 90.0 1.9572 179719.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3623 6.0 1 90.0 1.9458 179719.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3623 7.0 1 90.0 1.9345 179719.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3631 8.0 0 92.6 2.6086 194735.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3631 9.0 0 92.6 2.4401 194735.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3631 10.0 0 92.6 2.2825 194735.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3631 11.0 0 92.6 2.1351 194735.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3631 12.0 0 92.6 1.9972 194735.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3631 13.0 0 92.6 1.8682 194735.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3631 14.0 0 92.6 1.7476 194735.0\n",
      "I am working on tract number 3640 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3641\n",
      "we have 2 non-opposing shorted wedges for tract no 3647\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3652 16.0 2 111.2 5.405 402053.9\n",
      "we have 2 non-opposing shorted wedges for tract no 3654\n",
      "we have 2 non-opposing shorted wedges for tract no 3656\n",
      "we have 2 non-opposing shorted wedges for tract no 3657\n",
      "we have 2 non-opposing shorted wedges for tract no 3658\n",
      "we have 2 non-opposing shorted wedges for tract no 3659\n",
      "I am working on tract number 3660 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3660\n",
      "we have 2 non-opposing shorted wedges for tract no 3661\n",
      "we have 2 non-opposing shorted wedges for tract no 3663\n",
      "we have 2 non-opposing shorted wedges for tract no 3669\n",
      "I am working on tract number 3680 of 4706 tracts\n",
      "I am working on tract number 3700 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3713 4.0 1 37.8 2.2736 330893.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3713 5.0 1 37.8 2.2067 330893.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3713 6.0 1 37.8 2.1418 330893.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3713 7.0 1 37.8 2.0788 330893.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3713 16.0 1 37.8 2.3482 330893.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3713 17.0 1 37.8 2.2791 330893.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3713 18.0 1 37.8 2.2121 330893.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3713 19.0 1 37.8 2.147 330893.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3713 20.0 1 37.8 2.0838 330893.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3713 1 330893.2042332826 2.2914\n",
      "I am working on tract number 3720 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 8.0 1 36.3 4.2188 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 9.0 1 36.3 3.9625 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 10.0 1 36.3 3.7217 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 16.0 1 36.3 4.4539 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 17.0 1 36.3 4.1832 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 18.0 1 36.3 3.929 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 19.0 1 36.3 3.6903 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 25.0 1 36.3 4.7407 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 26.0 1 36.3 4.4526 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 27.0 1 36.3 4.1821 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 28.0 1 36.3 3.928 219045.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 29.0 1 36.3 3.6893 219045.3\n",
      "loop31.0, tr3734,wedgePops564646.89, 108333.3, 53093.6, 201511.5, 201708.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0524 \n",
      "   targetWP, latest drx4 are tWP,dr, 201658.0,0.6759, 201658.0,-1.7326, 201658.0,0.0007, 201658.0,0.0091\n",
      "loop32.0, tr3734,wedgePops681491.99, 108333.3, 169938.7, 201511.5, 201708.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0624 \n",
      "   targetWP, latest drx4 are tWP,dr, 201658.0,0.6759, 201658.0,1.2752, 201658.0,0.0007, 201658.0,0.0091\n",
      "loop33.0, tr3734,wedgePops683198.95, 108333.3, 171645.7, 201511.5, 201708.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0624 \n",
      "   targetWP, latest drx4 are tWP,dr, 201658.0,0.6759, 201658.0,0.2091, 201658.0,0.0007, 201658.0,0.0091\n",
      "loop34.0, tr3734,wedgePops730598.54, 108333.3, 219045.3, 201511.5, 201708.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0624 \n",
      "   targetWP, latest drx4 are tWP,dr, 201658.0,0.6759, 201658.0,2.0387, 201658.0,0.0007, 201658.0,0.0091\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3734 1 219045.26001607746 5.2556\n",
      "loop35.0, tr3734,wedgePops677472.23, 108333.3, 165918.9, 201511.5, 201708.4, Overedge?1, 1, 0, 0, ,Satisfied?0011,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 219527.5,0.6759, 219527.5,-2.6278, 219527.5,0.0007, 219527.5,0.0091\n",
      "loop36.0, tr3734,wedgePops726630.2, 108333.3, 165918.9, 216287.5, 236090.4, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 219527.5,0.6759, 219527.5,-2.6278, 219527.5,0.0159, 219527.5,0.0419\n",
      "loop37.0, tr3734,wedgePops721942.38, 108333.3, 165918.9, 218902.4, 228787.7, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 219527.5,0.6759, 219527.5,-2.6278, 219527.5,0.0028, 219527.5,-0.016\n",
      "loop38.0, tr3734,wedgePops704791.44, 108333.3, 165918.9, 219403.7, 211135.4, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 219527.5,0.6759, 219527.5,-2.6278, 219527.5,0.0005, 219527.5,-0.0165\n",
      "loop39.0, tr3734,wedgePops711805.4, 108333.3, 165918.9, 219403.7, 218149.4, Overedge?1, 1, 0, 0, ,Satisfied?0010,yoyo?0002 \n",
      "   targetWP, latest drx4 are tWP,dr, 219527.5,0.6759, 219527.5,-2.6278, 219527.5,0.0005, 219527.5,0.0063\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.32702 78037.515 108333.3498 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.62778 219045.26 165918.9479 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.90863 218902.422 219403.7446 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.37352 211135.3959 218149.3624 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 3740 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3750 3.0 1 90.0 2.2728 206921.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3750 1 206389.85803544932 2.0494\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3750 1 25481.79961871676 1.0247\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3750 1 206452.08547291302 2.0517\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3750 1 66146.36971379534 1.5382\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3750 1 158751.60253230407 1.795\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3751 5.0 1 40.9 2.143 207010.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3751 1 184394.09138952044 1.8505\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3751 1 25070.442661124165 0.9253\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3751 1 202239.4144899426 1.8644\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3751 1 143216.2018598779 1.3948\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3751 1 146733.13699052756 1.6296\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3751 1 153703.67948836804 1.747\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3751 1 163793.54738654618 1.8057\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3751 1 168443.56863060483 1.8351\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3751 1 183402.70907169467 1.8497\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3751 1 173499.158747895 1.8424\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3752 5.0 1 38.8 2.1314 207844.2\n",
      "I am working on tract number 3760 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3766 11.0 1 51.9 2.667 224487.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3766 17.0 1 51.9 2.6755 224487.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3766 1 224487.64252075448 3.4603\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3767 4.0 2 90.0 2.5338 244455.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3769 4.0 0 84.9 2.5788 196174.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3769 5.0 0 84.9 2.3987 196174.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3769 0 196174.53387822746 2.3635\n",
      "we have 2 non-opposing shorted wedges for tract no 3778\n",
      "I am working on tract number 3780 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3780\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3782 1 411051.1561492402 2.1229\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3783 8.0 1 37.2 2.3675 393351.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3797 9.0 1 37.6 2.3414 399507.9\n",
      "I am working on tract number 3800 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3813\n",
      "I am working on tract number 3820 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3823 0 225097.1741218653 2.1564\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3825 1 183198.37461453237 2.2738\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3825 1 38705.041478815925 1.1369\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3825 1 183197.16266898846 2.2738\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3825 1 76140.29610791964 1.7053\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3825 1 91941.65443378405 1.9896\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3825 1 119405.68510515835 2.1317\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3825 1 139910.8876448093 2.2027\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3825 1 153404.07709910307 2.2382\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3827 1 272408.07838556194 2.6595\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3827 1 70352.29618516806 1.3297\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3827 1 264231.25496394647 2.6298\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3827 1 125855.45917818116 1.9798\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3827 1 244855.57268496155 2.3048\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3827 1 232226.8122690057 2.1423\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3827 1 210815.02997590363 2.061\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3827 1 148674.57527999 2.0204\n",
      "we have 2 non-opposing shorted wedges for tract no 3839\n",
      "I am working on tract number 3840 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3842\n",
      "we have 2 non-opposing shorted wedges for tract no 3846\n",
      "I am working on tract number 3860 of 4706 tracts\n",
      "I am working on tract number 3880 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3882\n",
      "we have 2 non-opposing shorted wedges for tract no 3896\n",
      "we have 2 non-opposing shorted wedges for tract no 3897\n",
      "I am working on tract number 3900 of 4706 tracts\n",
      "I am working on tract number 3920 of 4706 tracts\n",
      "I am working on tract number 3940 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3954 8.0 3 90.0 2.8459 191370.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3954 3 191370.62120348267 3.3186\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3954 26.0 0 83.6 2.598 205022.2\n",
      "loop31.0, tr3954,wedgePops739781.39, 205022.2, 178241.9, 178077.9, 178439.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4123 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.2406, 178326.8,-0.0187, 178326.8,0.002, 178326.8,-0.0007\n",
      "loop32.0, tr3954,wedgePops739781.39, 205022.2, 178241.9, 178077.9, 178439.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5123 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.502, 178326.8,-0.0187, 178326.8,0.002, 178326.8,-0.0007\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3954 32.0 0 83.6 2.8176 205022.2\n",
      "loop33.0, tr3954,wedgePops739781.39, 205022.2, 178241.9, 178077.9, 178439.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5123 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.2847, 178326.8,-0.0187, 178326.8,0.002, 178326.8,-0.0007\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3954 33.0 0 83.6 2.5328 205022.2\n",
      "loop34.0, tr3954,wedgePops739337.4, 204578.2, 178241.9, 178077.9, 178439.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5123 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.256, 178326.8,-0.0187, 178326.8,0.002, 178326.8,-0.0007\n",
      "loop35.0, tr3954,wedgePops570165.2, 35406.0, 178241.9, 178077.9, 178439.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5123 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-1.1384, 178326.8,-0.0187, 178326.8,0.002, 178326.8,-0.0007\n",
      "loop36.0, tr3954,wedgePops689414.07, 154654.9, 178241.9, 178077.9, 178439.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6123 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.8015, 178326.8,-0.0187, 178326.8,0.002, 178326.8,-0.0007\n",
      "loop37.0, tr3954,wedgePops692126.26, 157367.1, 178241.9, 178077.9, 178439.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6123 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0979, 178326.8,-0.0187, 178326.8,0.002, 178326.8,-0.0007\n",
      "loop38.0, tr3954,wedgePops739781.39, 205022.2, 178241.9, 178077.9, 178439.3, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6123 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.5107, 178326.8,-0.0187, 178326.8,0.002, 178326.8,-0.0007\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3954 0 205022.1985646584 2.5485\n",
      "loop39.0, tr3954,wedgePops575516.83, 40757.6, 178241.9, 178077.9, 178439.3, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 224183.2,-1.2743, 224183.2,-0.0187, 224183.2,0.002, 224183.2,-0.0007\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.27427 205022.1986 40757.6431 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 4.00042 178803.3856 178241.9399 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.79504 177268.8786 178077.9248 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.51109 178985.379 178439.3264 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr3954,wedgePops647546.12, 40757.6, 188864.9, 206118.2, 211805.5, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 224183.2,-1.2743, 224183.2,1.0514, 224183.2,0.0901, 224183.2,0.0482\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.27427 205022.1986 40757.6431 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 5.05184 178241.9399 188864.859 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.88514 178077.9248 206118.1506 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.55931 178439.3264 211805.4674 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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lyrBi9njsviytdayPklJy6PgZJs1fzh+371G5fFlWz51IhbKltI6m6LhEC7gQwgwwkFKGJEw3BCYCO4BuwPSE79vTMqiifMyNW3eYNH85h0/4UaRAftynj6Fpg1o6v6Pv2p+3mTjPnaOnz2NbyAbPWeNoVLeGzudWdENStsDzAlsT/qAMgQ1Syv8JIc4Am4QQvYD7QLu0i6ko7+cf8ILZ7mvw3r4XCzNTxg12pnv7Fhhl0+1Lgj159pzZy9bgs2MvVhbmTBjWn65tm5Eta1atoyl6JNECLqW8DbzT9V1K+RxokBahFCUxEZGRuK/bzJK1m3j1KooeHVoyuFcnclhbah3to8IjIljm5cuStZuIiYmlT8fWDOrVEWtLC62jKXpIHY+k6JW4uDh89xxgxpLVPPEPoHG9Gowe0JtihQtoHe2jYmNj8d19gJlLV/Pk2XOaNqjF6AG9sC1oo3U0RY+pAq7ojWN+F5g4z4MrN2/xVZmSLJ48kqoVy2kdK1FHT59nops71/64TYWypVg2bQyVyn+hdSwlA1AFXNF5t+7eZ/J8T/YfPYlN3twsnPQD335TT+cvCfbnnftMmr+cg7+fopBNXpZMHU0LhzpqB6WSalQBV3TW85eBzPVYh9eWXZgYGzPKtSe9HFthYmykdbSPCnjxkjkeXqzfugdTY2PGDOxNjw7fYmyk2ztWFf2jCriicyJfRbHSZxsLVm4gPDKSTq2aMMy5C7lyZNc62kdFRL7C03sLi1ZvJCIykq5tmjPUuTM5rK20jqZkUKqAKzpDSsmOfYeZumglDx4/pUHNKowd1IcSRXW7BUNcXBzb9h5i+uJVPHziT8Pa1fhxYG+K2xbSOpqSwakCruiEMxeuMmHeMs5fvUmZksXwGTuDWpUraB0rUafOX2biPA8uXLvJl6WK4zZ+BNXt3znqVlHShCrgiqbuPnjElAWe7Pn1d/Llzsncn4bTtkkDnb8k2O37D5m60JNfDh0jX55cuI0fQZsmDXR+x6qSsagCrmjiZVAw81dsYPWmHRgaZmF436707dwGUxPdviTYi8Bg3DzXsebnnWTLlpXv+3fHuVNrTIyNtY6mZEKqgCvpKio6mjU/78TNcz1BIaE4tviGEf27kTdXTq2jfdSrqChWbdrBghUbCAkLx6llI4b37UqeXDm0jqZkYqqAK+lCSsmeX39n6sIV3H3wiNpVKjJ2sDNlShTTOtpHSSnZdfAo0xau4N7Dx9Srbs+YgX0oVbyo1tEURRVwJe2dv3KDiW4enL5whZLFiuA1fzL1qlfS+RNazl6+zsR57vhdukbp4kXZsGgqdaraax0rXURFRxEbG4uJsW4PaWV2qoAraebB46dMW7SSbXsPkSuHNdNHDcSpZWMMDXV7B+X9h4+ZtmglO/YfIU/OHMwaM4QOzRvq/I7V1DRh7gi27fXBa8FOKpatrHUc5QNUAVdSXXBoGItW+eDpvQUhBAN7OuHSrQPmZqZaR/uooJBQFqzcwEqf7RgYGDC4dye+69oeM9PMtRUqpeR/R3YQGPySDv2/YeWczdSqXF/rWMp7qAKupJromBjWb93DHHcvXgQG0abJ1/zwXXcK5Hvv5VJ1RnRMDF6+u5i7fB2BwSG0a+bAiH7dsMmbW+tomrjz9y2e+D9kaJ8x7Pl1K10HtWDJ1HU0rvet1tGU/1AFXEkxKSUHfj/F5PnLuXX3b6pVLMe4Ic6UK11S62gfJaVk35ETTF7gye37D6hRqTzjBjlTtlRxraNp6tiZwwC0auxET0dXugxqgfMPjswe60GH5l21Daf8iyrgSopcuXGLifM9OHbmAsUKF2Tl7PE0rFNN53dQXrr+BxPneXDi3CWK2xZi9byJfF2zis7nTg/H/A6RL08BihYqjhCCjUv+R8/hbRk6oTchoUH0dhqgdUQlgSrgyid57B/AjCWr8N19AGtLCyYN/44ubZuR1VC3/6QePvFnxpLVbN5zgBzWVkz5wZVOrZrofO70IqXkuN8R6lZr+ObNzMzUnLVu23H5sTM/zRlGUPBLhjqPVW92OkD91SrJEhYewZK1m1jm5UtcXBz9OrdlQE8nrCzMtY72UaFh4SxavZHlGzYjpcS1ewdcujtiaW6mdTSdcvOvqzx/+Ywaler9a75RNiOWTfNmxJR+zF0+maCQQMYPna1aB2hMFXAlSWJjY9m4cx+zlq7B//kLWjjUYZRrTwoXyK91tI+KiYnFe/svzHZfS8CLQFo1qsdIl54UzJ9X62g66ZjfYQBq2Nd5Z5mhoSFzxnpgaW6Np/cCgkODmD3GHUP16UUzSX7lhRBZAD/goZSymRDiK2AZYA7cBTpJKYPTJKWiqcMn/Jjk5sGNv+5iV64MnrN/wu7L0lrH+igpJYeOn2Hy/OXcvH2PyuXLsnruRCqULaV1NJ127MwhihQoRsH8Rd673MDAgPFDZ2Ftac1s94mEhAaxeMo6jI1ULxgtJOetcxBwHXh92W9PYLiU8ogQoicwAhibyvkUDd24dYdJ85dz+IQfhQvkY9n0MTRrUEvnxz6v/XmbSW4e/HbqHLYFbVg+cxyN69XQ+dxai42N5eS5ozRt0Pqj9xNCMKTPGCwtrBk3eyhdB7dk5WxfzM0s0imp8lqSCrgQoiDQFJgCDE2Y/TnwW8L0fmAvqoBnCP4BL5jtvgbv7XuxMDNl7GBnerRvgVE23b4k2NOA58xaugafHXuxsjBn/NB+dGvXnGxZs2odTS9c/eMiQSGB1LCvm6T793J0xdLcimGTnHH8rhFeC3aS3Uo190pPSd0CdwO+B95+i70CtAC2A+2A915+RAjhDDgDFC6s21dWyewiIiNxX7eZJWs38epVFD06tGRwr07ksLZM/MEaCo+IeJM7OjqG3k6tGNSrI9mtdDu3rjl25hAA1ZNYwAHaNeuCpbkV/UZ1pE2f+mxYvId8uW3SKKHyX4nuQhZCNAP8pZRn/7OoJ+AihDhLfGGPet/jpZQeUkp7KaV97tyZ88w2XRcXF8fPu/ZTs3VPZi1bQ+0qFTn0sycTh/XX6eIdGxvLxh17qdW6J7Pd11K3mj2HfT0ZP7SfKt6f4JjfYUoULUWeXPmS9bhv6rbAa8FO/n58j1a963Hvwe00Sqj8V1K2wGsALYQQTQBjwFIIsU5K2RloCCCEKEn8EIuiZ477XWSimzuXb9ziqzIlWTx5JFUrltM6VqKOnj7PRDd3rv1xmwplS7Fs2hgqlf9C61h6KzommlPnf6d9s08707JmpXpsWrqPzoOa06p3PTYs2k2p4mVTOaXyX4lugUspR0kpC0opbQFH4FcpZWchRB4AIYQBMIb4I1IUPXHr7n26DxlHu34jeP4yiIWTfmDX6gU6X7z/vHOfroPH4vjdDwSHhLFkyih2rpqvincKXbjqR3hEGDUq1f3k56hQthKb3Q8A0Ma5AeeunE6ldMqHpOQATichhEvC9BZgVSrkUdLY85eBzPVYh9eWXZgYGzPSpQe9nVpjYmykdbSPCnjxkjkeXqzfugdTY2N+HNCbno7fYmyk2ztW9cWxM4cQQlC1Yu0UPU+p4mXZ6nkIJ5cmqpNhOhBSynRbmb29vfTz80u39Sn/iHwVxUqfbSxYuYHwyEg6tWrCMOcu5MqRXetoHxUR+QpP7y0sWr2RiMhIurRuxlDnzuTMbq11tAylXb+GBAW/ZN+GM6nyfE8DHtPRpQm37//J0mnraVS3Zao8b2YlhDgrpXznaiLqFKoMTkrJjn2HmbpoJQ8eP6VBzSqMGdibksXef6KGroiLi2Pb3kNMX7yKh0/8cahVlTGDelPcVh3JlNoiX0Vy9tIJurXrn2rPmTdXfnw9DtJlUAv6fN+BOeM8Pnl8XfkwVcAzsDMXrjJh3jLOX71JmZLF8Bk7g1qVK2gdK1Gnzl9m4jwPLly7SdnPizNv/HBq2JfXOlaGdfbySV5FvUrR+Pf7ZLfK8aaT4ZDxvQkJDaaXo2uqriOzUwU8A7r74BFTFniy59ffyZsrB3PHDaNt0691/pJgt+8/ZOpCT345dIx8eXLhNn4EbZo0UA2T0tixM4fIkiULVcrXTPXnNjM1Z828bbj82Jlxs4cSFPySIX3GqLNiU4kq4BnIy6Bg5q/YwOpNOzA0zMIw5y7069IWUxPdviTYi8Bg3FasZ82mHWTLlpUR/brRt3MbTIxVf430cOzMYcqVtsPCPG2OnTc2MsZ9ug/DJ/dljsckAkMCGT9klnpjTgWqgGcAUdHRrPl5J26e6wkKCaVD84aM6N+dfLlzah3to15FRbF60w7mr9hASFg4Ti2/YXjfbuTJpU7HTi9h4aFcuHqGfl2HJn7nFDA0NGTuuOVYWlizwnshIaFBzPpxmepkmELq1dNjUkp+OXSMKQs9ufv3I2pXqcjYwc6UKVFM62gfJaVk18GjTFu4gnsPH1Ovuj1jBvahVPGiWkfLdE5fOEZMbAw17OslfucUMjAwYMLQ2VhbWDPHYxLBIYGqk2EKqQKupy5cvcmEee6cvnCFksWK4DV/MvWqV9L5scWzl68zcZ47fpeuUeozWzYsmkqdqu8cHaWkk2NnDpHVMCuVvqqWLusTQjDUeSyWFtb8NGcY3YZ8y8rZvpiZ6vYFQXSVKuB65sHjp0xfvJKt/ztErhzWTB81EKeWjTE01O0dlPcfPmbaopXs2H+EPDlzMGvMEDo0b6jzO1YzumN+h7ErVxUTY9N0XW9vpwFYWlgxfFJfOnzXCK/5O1Qnw0+gCrieCA4NY9EqHzy9tyCEYEAPJ1y6tcdCxy8JFhQSysKV3qzw2YaBgQGDe3fiu67tMTPV7R2rmUFg8Euu3LzAkN4/arL+9s26YmluRf9RnWjr3IANi/eQN5duX+FJ16gCruNiYmJZt3U3c9y9eBEYROvGDRjp0oMC+fJoHe2jomNi8PLdxdzl6wgMDqFt06/5vn93bPKqjpS64tS5o8TFxb1z/cv01KhuS9bO30HPYW1o1ase3ov3UKSgbu/D0SWqgOsoKSUHfj/F5PnLuXX3b6pW/JJxg/vyVZmSWkf7KCkl+46cYPICT27ff0B1+6/4aXBfypYqrnU05T+O+R3G2MiE8l9U0jRHrcr12bh0L10Gxncy9F68h88/U83JkkIVcB105cYtJs734NiZCxQrXJCVs8fTsE41nd9Been6H0x0W86Jsxf5rEhBVs2dgEOtqjqfO7M6duYQlcvXwCib9o3MKpatzGaPg3R0bUpr5wasm7+TCmW1fWPRB6qA65DH/gHMWLIK390HsLa0YNLw7+jSthlZdfxY2YdP/JmxZDWb9xwgh7UVU35wpVOrJjqfOzMLeOHPjb+u0qqxk9ZR3njdydDRpTEdvovvZFhTw+EdfaD+w3RAWHgES9ZuYpmXL3FxcfTt3IaBPTtiZaHbh1aFhoWzeM1GPNZvRkqJS7cOuPZwxFLHd6wqcPzsESB5l09LD0UKFmOr5yE6ujSh66AWLJ26nm/qttA6ls5SBVxDsbGxbNy5j1lL1+D//AXNHeow2rUnhQvo9p74mJhYfHb8j9nua3n2/CXfflOPkS49KGSTvEtxKdo5duYw5mYWlCtVUeso78iX2wZfj4N0HdSCPj90YM5YD9o166J1LJ2kCrhGjpz0Y5Lbcq7fuoNduTIsnzUO+3JltI6VqEPHzzDJzYObt+9R6asvWDlnPBXLltY6lpJMx/wOUbViLZ09lT2HdU58EjoZDh7fi5DQYHo6uiT+wExGN397GdiNW3eYvGA5h477UbhAPpZNH0OzBrV0fkfftT9vM3n+co6cPIttQRs8ZoylSf2aOp9bedejpw+4c/8WXdv01TrKR5mbWbDWbTsuP3Zm7OwhBIW8ZHDvH9Xf3FtUAU8n/gEvmO2+Bu/tezE3NWHsYGd6tG+BUTbdviTY04DnzFq6ho0792FpbsZPQ/rSvX0LsmXNqnU05RMd9zsMQHX7OtoGSYLXnQyHTXJmtvtEAoMD+WnITNXJMIEq4GksIjIS93WbWbJ2E69eRdGjfQsG9+5MDuu0ad2ZWsIjIt7kjo6OoZfjtwzq1ZHsVrqdW0ncMb/DWFvloEwJ3b6A9WuGhobM+8kTKwtrPL0XEBwaqDoZJlCvQBqJi4tj856DzFi6isdPA2hcrwajXHvxWZGCWkf7qNjYWHz3HGTmklU8efacJvVrMnpAL4oWKqB1NCWVnLl4nM+LldGroQgDAwMmDJuDlYU1c5dPJiQ0iMVT1unEMeyaklIm6QvIApwHdiXcLg+cBC4AfkDlxJ7Dzs5OZgbHzlyQ33TqL23sHGSjzt/JE2cvah0pSX47dU46OPWTNnYOsmlXV3nq/GWtIylpYMzMwdLGLqscP3eEjIuL0zpOsi3fsEDa2GWVHfo3kqFhIVrHSReAn3xfXX7fzPfeEYYCG94q4PuAxgnTTYDDiT1HRi/gf965L7sNHitt7BykfZOOcvOeAzI2NlbrWIn64/Y92WXQGGlj5yArN+sst/7yq17kVj5NbGzsmyI+dEIfGRMTo3WkZPPZsUYWrGQkm3WvKV8EPtc6Tpr7UAFP0hCKEKIg0BSYklDIASTwekDUCnj0yR8D9Nzzl4HM9ViH15ZdmBgbM9KlB72dWmNirNsf7wJevGSOhxfrt+7B1NiY0a696OXUCmMj3d6xqqSMgYEBE4fPxcrSmnnLpxAcGsSiyWv1ajiiQ/OuWJpb8t3ozpm7k+H7qvp/vwBfwA6oyz9b4KWB+8DfwEOgyAce60z8EItf4cKF0/VdK61FRL6Si1dvlJ/XbikLVvpG/jB1vnz2/IXWsRIVHhEpF67yliVrt5SFKn8jR09fKANevNQ6lqIBj/XzpY1dVun4XWMZFh6qdZxkO3LygCxe01pWb1lK3ntwW+s4aYZPHUIBmgFLEqbfLuALgDYJ0+2BA4k9V0YZQomLi5Pb/verrNyss7Sxc5CdB/4ob/51V+tYiYqNjZVbfjkoKzXtJG3sHGS3wWPln3fuaR1L0ZjP9tWyYCUj2bxHLfkySPc3QP7L79JJWaZeHlnhm8Lyxq0rWsdJEykp4NOAB8Bd4AkQDqwDggCRcB8BBCf2XBmhgJ8+f0U27TZA2tg5yAaOfeWRk2e1jpQkJ89dkk26uEobOwfZsGN/efT0Oa0jKTpk98Et0raqmWzgWFE+ffZY6zjJdu3PS7J8w0KyTP288vyVM1rHSXWfXMDlv4v521vg14G6CdMNgLOJPV6fC/idvx/KPt9PlDZ2DrLCNx2kz/b/6cXOn7/uPZC9ho+XNnYOsmJjJ7lx5161g1J5rzfDEd+Wlvcf3tE6TrLd+fuWrNqipCxRK7v8/cwhreOkqrQo4DWBs8BF4BRgl9jj9bGAvwwKluPnLpNFqjSWn9VoJue4r5Vh4eFax0rUi8AgOXb2Elm4ciNZvGZzOXe5lwyPiNA6lqLj/C6dlKXr5pYVG9vKm39d1TpOsj32fyjrtisni1Yzl3sP79A6Tqr5UAF/PQSSLuzt7aWfn1+6rS8loqKjWfPzTtw81xMUEkqH5g0Z0b87+XLn1DraR72KimL1ph3MX7GBkLBwnFp+w7C+XcmbS7dzK7rj+q3LdHRpSnRMNOsX7uKrMnZaR0qWF4HP6TKwOZdvnmfuuOW0bdpZ60gpJoQ4K6W0f2fB+6p6Wn3pwxZ4XFyc3H3wqKz+bTdpY+cgO/T/Xl65eUvrWImKi4uTO/cfkdVadJU2dg6yo+soee3PjLtXXklbt+//Kas0LyFL1s4hj505rHWcZAsJDZZt+zpIG7uscoX3Iq3jpBipMYSS0i9dL+Dnr9yQrXoPkTZ2DrJuu97ywNFTenGm2tnL12TLnoOljZ2DrN++jzx0POPtxFHS36OnD/4ZjjiyU+s4yRYRGSF7DG0tbeyyyrnLJ+vF//KHqAL+EX8/eiJdfpwqbewcZDmHdnKt704ZHa37OyjvPXgk+42cLG3sHORXDdvLdVt260VuRX88fxkgm3SpJgtVNpab96zXOk6yRUdHy4Hjekgbu6zypznD9baIf6iAZ+pmVsGhYSxa5YOn9xaEEAzo4YRLt/ZY6PglwYJCQlm40psVPtswMDBgUK+OfNe1PeZmplpHUzKYHNY52bh0Lz2GtWHA2O4EhwTRvX1/rWMl2etOhpbmVizfMJ/gkEBm/rg0w3QyzBg/RTLFxMSybutu5np48fxlEK0bN2CkSw8K5MujdbSPio6JYd3m3czx8CIwOIQ2TRrww3c9sMmbW+toSgZmbmaB1/wd9B/VkR9nDiIoJJCBPUfqTTfD/7YOCAkNYtEUL71qHfBB79ssT6svrYdQ4uLi5L7fTsjabXpKGzsH2brPUHnh6k1NMyVFXFyc3Hv4uKzVuoe0sXOQbfsOl5eu/6F1LCWTiYqOkq5jukkbu6xywrzv9XI44u3WAfrUyZDMPoRy5eZfTHRz59iZCxQtXIAVs3/imzrVdX4r4vKNP5kwz4MTZy/yWZGCrJo7AYdaVXU+t5LxZDXMyvwJK7GysMZ93TyCQwKZMXoJWbJk0TpakvXpOBBLcyuGT+6Lo0tjvObvwNoyu9axPlmGL+CP/QOYuXQ1P+/aj5WlOROH96dLm2Y6f0mwh0/8mbFkNZv3HCCHtRVTvnelU+smZM0gY3eKfjIwMGDSiHlYWVrj5jmV4NAgFk5ao1fDER1adMPC3BKXH7vEdzJctIc8ufJpHeuTZNgTecLCI1iydhPLvHyJi4ujp2NLBvRwwtrSIl3W/6lCw8JZvGYjHus3Excn6e3UigE9nbDU8R2rSubjsX4+E+aNoE5VBzxnbcLURL/+Rn87dZBew9uSJ2c+vBfvoXCBolpH+qAPnciT4Qp4bGwsG3fuY9bSNfg/f0FzhzqMculJkYK63Ss4JiYWnx3/Y7b7Wp49f0nLhnUZ5dqTQjb6uWWgZA4+21czYko/Kpatwhq3bXo3HHH28im6DGyOibEp3ov3ULJYGa0jvVemKOBHTvoxyW0512/doeKXpflpSF/sy+nmL+Rth46fYZKbBzdv36PSV18wbogzFcuW1jqWoiTJ7oNbcB3Tlc9sP8d70R5y58yrdaRkebt1wLoFOyn/xbtnrGstQxfwG7fuMHnBcg4d96NwgXyMcu1F869r6/yOvuu37jDJzYMjJ89iW9CG0QN60aR+TZ3PrSj/9dvJA/Qc3pa8uW3wWbyHQja2WkdKljt/38LJpQkvAgNYPXcr1e3raB3pXzJkAfcPeMFs9zV4b9+LuakJg3p3okf7Fhhl0+1Lgj0NeM7sZWvx2bEXS3MzBvXqSLd2zXU+t6J8jN+lk3Qd1AITEzN8Fu+hRFH9+hT52P8hHV2bcO/BbZZN96Zh7WZaR3ojQxXwiMhI3NdtZsnaTbx6FUW3ds0Z3LszOawtE3+whsIjIt7kjo6OScjdiexWup1bUZLq2p+X6OjalJiYGL3vZDjvJ0/aNOmkdSQgg3QjjI2NlZt27pN2TZykjZ2D7DnsJ3nr7t8pes70EBsbK3127JUVGzlKGzsH2Xv4BHn7/gOtYylKmrh9/09ZuVlxWbJ2Dnnc74jWcZLt7U6GK30Wax1HSpkBmlkdO3NBftOpv7Sxc5CNOn8nj/td/OTnSk9HT5+TDk79pI2dg2zSxVWePHdJ60iKkuYePX0g67T9UharbiH3/bZL6zjJFhEZIbsPaSVt7LLKecunaH7W6YcKuF4MoWzY9gsjJs8DQAhB+TIlMTcz/efL1BQzMxPMTU3emjaNv22esNzUJOG+July5tifd+4zecFyDhw9RYF8eRjt2osWDetgYGCQ5utWFF3wIjCAzgObc/XmRdwmrKBVIyetIyVLTEwMQyf2YfOe9Th3Gsy4wTM0O8DgQ0MoenFan92Xpenl+C0hYeGEhoUTFh5BaHgE/gEvCA2PiL8dFk5MbGySns/YyAhzM5M3xd/czASz1wU/YdrC7N9F3yzh++s3iPjlphgbZfvXLzXgxUvmeHixfuseTI2NGe3ai15OrTA2Ujsolcwlh3UuNi75p5NhUEgQ3dv10zpWkhkaGuI2fgWW5lZ4rHd708lQl1oH6MUWeFJIKXkVFU1YeHhCoY8gLDz+e2h4fNEPCQ17U/z/eSMIJywsIv4xCfcLDQsnIvJVktabJYvBv7bwHz19RkRkJJ1bN2WYcxdyZrdOk59XUfRFRGQE/Ud1ZP/R3fzw3UQG9PhBrw6VlVIy230Cbp5TaVK/FYsmr0331gEZ6iiU9BAbG0tYRORbRf/fbwph4RGEhIXFfwII++eNwMzMBJduHShRtLDWP4Ki6IzomGiGTujNll+86ddlKGMGTtOrIg7/tA6oXeVrVsz+OV1bB6R4CEUIkQXwAx5KKZsJITYCnycstgYCpZTlUyGrTsiSJQuW5maqB4mipIL4ToarsDS3ZpnXXIJDApk+arFODUckxrnTICzNrRgxpR+OLo1Z67Zd89YByRkDHwRcBywBpJQdXi8QQswBglI3mqIoGYmBgQGTv3fDytKa+SumERwaxIKJq/Wqk6Fjy+5YWljFdzLs+zUbFu7WtJNhkg6JEEIUBJoCnu9ZJoD2gHfqRlMUuHLzKWt8z7J5zxX2Hv6D8XMP4B8QqnUs5RMJIfi+/wTGDZ7JrgOb6TG0NeERYVrHSpYm9VuxZt427v79F6361OfvR3c1y5KkMXAhhC8wDbAAhkspm721rDYw933jMwnLnQFngMKFC9vdu3cvNXIrmYTrmO1s/d/Vd+bfOfED2bLqz8dv5V1vdzJcO387VhbWWkdKljetA9Khk+GHxsAT3QIXQjQD/KWUZz9wFyc+svUtpfSQUtpLKe1z51bXblSSZ+JwByp8YQNAL0d7HGqVAKDH0J8Jj4jSMpqSQo4tu7N06nouXvOjbd+vefb8qdaRksW+XFV8PQ4QGxdL6z4NuHjtQyUy7SRlCKUG0EIIcRfwAeoLIdYBCCEMgdbAxjRLqGRqOaxN8VniRI1KRVjh40ftKrbMGduU307doaOrD0EhkVpHVFKg2ddtWOO2jTv3b/Ft73o8eKxfn9DLlCjHVs9DmJtZ0L5/Q477HUnX9SdawKWUo6SUBaWUtoAj8KuUsnPC4q+BG1LKB2mYUcnkzM2MWOvWgUZ1SzJ29n4e+wezdOq3XLj6iHZ91/PsuRoT12d1qjrgvfgXXgYG0LJXXf68c13rSMlStFBxtnoeIn+eAnQe2Ix9v+1Kt3Wn9LxuR9TOSyUdGBsZ4j69NW2bfsls96OcufiA1fPac/v+C1r1WceDx+ogKH1W6atq8cMRsTG07tOAS9fPaR0pWfLnKcCW5b9SqnhZeo9ox5ZfNqTLepNVwKWUh9/egSml7C6lXJb6sRTlXYaGBsz7qRm9HO3x9D7D9n3XWL+wAy9ehvNt77XcuhugdUQlBcqUKMeW5b9iamJGu34OnDx3VOtIyfK6dUDl8jUZMLY7qzctTfN1qjMxFb0jpWSux1HmLv+dJvU+x6V7NboP/ZnY2Dg2LHLiy1LqOqL67NHTB1RqWuzN7c+LlcHMzAJzUwvMTc3fTJuZWvD8ZUFCw7KTNasR1e0FBfKZYWFmibmZOWamFpibWWBmYp6uJwy93Trg+/4TGNhzZIrPOlWn0isZjqf3aX6ac4BalW0ZM6gBvYb7EhQcyep57ahaUbUy0GdnLp7g2151aFi7GYZZDAkNDyU0PISwsBBCw0MJDslBSFgn4O2athF4/4CAibEp5mbxBT3+jcACMzPzN28EFuaW8W8Or4u+afyyfz3GLP6+RtmMEi3I8a0D+rDllw307TyEsYOmp6iIqwKuZEgbd15i+KTdlP/Chpk/NqbfyK08eByEx4zWNKhZXOt4Sip7GhDFrKX38N7u/6/5zRqY0KejIDYujNCwEELDguOLflgIYeEh/5oOeT0v4c3g9XREZHiSMhhmMXxTzC3MLN76RGD+r2JvamLGWl8PHj39G6eWPZj549JPbietCriSYf1y6Cbfjd5GscI5WDSlJUMn7OLaH/7Mn9Ccbxt9oXU8JRWER8Tivu4hC1Y+ICr6n5rl2CIPI/oXJl/ulJ+OHxMTQ1jEh4p+MGFh8Z8C4j8JJEyHBROaMB3/RpDwhhAeQux/2lsf9DlHqeJlPymbKuBKhvbbqTv0Gu5LnpzmeMxszU+z93Py/H2m/tCIrm0rah1P+USxsRLf3f7MXHqfJ8/+OXGrVmUrxg4uyhcldbPZnJSSyFcRCVv5IQghsC342Sc/nyrgSoZ39vJDug7aiFE2Q1bPa8ccj6McOHqLUa51ce1eXet4SjIdPR3IJLe7XP3jn14pJYqaMHaQLfVrZNe7drQpoddX5FGUpLD7sgC+Hp3p6OKNk6sPq+e2w8LMiGmLDhMUHMnoAfUy1T+9vvrzTjiT5t/l4O8v38zLYW3IiH6F6fhtPgwN1e/wNVXAlQyldPE8bF3RFScXbzoN8GHF7DZYWhixZO1JgkIimTayEVmyqOuS6qKAF1HM8fibtb5P/jV/QI+CuHQrgIW5Klf/pV4RJcOxLZidrZ5dcHLxptvgTSyd2gorC2MWrDxOcOgrFkxsoToZ6pCIyFhW+DzGzfNvIiLj3sxv3Tg3I12KUCCf/vQLT29qU0TJkPLltmCzR2dKF89Dnx8281mRnIwd3ICd+6/Tc9jPRERGax0x04uLk2z5xZ86bc8zbdG9N8W7akVL9qwtx8JJJVXxToQq4EqGlcPalI1LO1KlQmEG/bSTbIYGzB7ThCMn7+Dk4q06GWro1Pkgmne/xICxf/LwSfwFxIsWNmbF7FL4upflqzIWGifUD6qAKxmauZkRXvM78E2d+E6GTwJCWfJWJ8OAF/p1NRh9d/t+BL1HXKd1nytcuBbfRdLKIgsThxfl140VaFQ3p9rRnAyqgCsZnrGRIR4zEjoZLvuNs5cesmpuO/6695xWvb14+ER1MkxrL4OiGTfnNrVan+OXQy/ezO/XxYbj2+3p5WhDtqyqHCWX2ompZAqvOxlamhuxfMNpgkMiWb/QkR5Df6Zlr7X4LHaiuG0urWNmOK+i4li96THzlv9NSNg/ZyY2d8jJKBdbihQ01jCd/lMFXMk0DAwEE4c7YGVpzLzlvxMS+ooNi5zoPnQTrXp7qU6GqUhKya6Dz5m28B73Hv6zr6Hilxb8NMQW+3KWGqbLOFQBVzIVIQTD+9bGysKY8XMPEBoexfqFTvQc9jPt+q5njVs7qlRQnQxT4uzlECbOu4PfpZA38woXMGKUqy3Nv1Zj3KlJDTopmVKfjpWZO64pv5+5y6jp/2ONW3vy5jano6sPB3+/pXU8vXT/YST9R92kRY9Lb4q3uVkWxg625fDPFWnhkEsV71SmCriSaXVo8RXu01tx5cYTBozZzrLprShRNBc9h/myfe9VrePpjaCQGCbPv0u1lmfZsf+fqyL1cszPie129OtcAKNsqtSkBfWqKplak/qlWOPWnnsPA+k9fDNzxjXF/qsCuIzZjtdm/bouY3qLjolj1cbHVP/2LEu9Hr6Z36huDn7bXJGJw4uRwzqrhgkzPtWNUFGI72TYZeBGTIwNWTW3HbPdj3Lwd9XJ8H2klOz/7QWTF9zjr3sRb+aXK23GuMFFqWZnpWG6jCnF3QiFEFkAP+Dh6wsbCyEGAK5ADLBbSvl9KuVVlHRl92UBNi+P72TY0dWH1fPaYWke38kwOOQVo1zrqvFb4PKNUCbMu8OJs8Fv5tnkzcZIlyK0apQbAwP1GqWn5ByFMgi4DlgCCCHqAS2BclLKV0KIPGmQT1HSTeniediS0ASro6sPK+e0xcLciMVrThAUEsnUH77JtJ0MHz55xYwl99i859mbecZGBgzuXYjeTvkxMVbNwbSQpL9GIURBoCng+dbs/sB0KeUrACml//seqyj6pGihHGz17EqBfJZ0HbSRetWK4dqjOuu2nMd1zHaiomMTf5IMJDQshhlL7lGlud+/ineXNvk4ucOOAT0KquKtoaRuTrgB3wNxb80rCdQSQpwSQhwRQlR63wOFEM5CCD8hhN+zZ8/edxdF0Sn58/zTybD395spWTQXYwbWZ8f+6/Qa5pspOhnGxEjWbXlCjVbnWLDyAa93ldWvkZ1DmyowfdRn5M6ZTduQSuIFXAjRDPCXUp79zyJDIDtQFRgBbBLvGSSUUnpIKe2llPa5c+dOjcyKkube7mQ4cNwOjI0MmTWmCYdO/EVHV2+CQzNuJ8NDx1/SsOMFfpj6FwEv4t+sSpcwxXvxF3jNL0PJYqYaJ1ReS8oWeA2ghRDiLuAD1BdCrAMeAFtkvNPEb52rZhJKhvG6k2HD2iUYM2sf/gGhLJ3WivNXMmYnw+u3wujoepXOA69x83Y4AHlzZWXuuOLsXVee2lWstQ2ovCPRAi6lHCWlLCiltAUcgV+llJ2BbUB9ACFESSAbEPCh51EUfWRsZMjymW1o06Qss5b9xrnLD1k5px237macToZPA6IYMfkWXzte4MjJQAAMswiGORfi9612dGiRlyxZ1NEluiglvVBWAiuFEFeAKKCbTM+DyhUlnRgaGuA2vjmW5kZ4rD9NcMgr1i3oQM9hvnzbywvvxU4Ut82pdcxkC4+IxX3dQxasfEBU9D//uo4t8jCif2Hy5VZXw9F16kQeRUkiKSWz3X/DzfMYTep9znfdqtF96CakhA0LHSmrJ50M4+IkP+/2Z+aS+zx5FvVmfq3KVowdXJQvSpppmE55nxSfyKMomZ0QghH96mBlYcKEef/uZNhWTzoZ/n4mkInz7nL1j3/G70sUNWHsIFvq18iuTlbSM5nzrARFSQHnTpWZMza+k+HoGfGdDPPkMqOjqw+/HvtL63jv9eedcLoNuUaH/lffFO8c1oZMG1mMA94VaFAzhyreekgVcEX5BI4t4zsZXr7+hAFjd+A+ozUliuaix9Cf2b7vmtbx3gh4EcWo6X9Rt915Dhx9+Wb+gB4FOb7Njq5t82NoqAq3vlIFXFE+UZP6pVgzrz13/35J7xHxnQztyhXA5cdtrNtyXtNska/iWLT6AVVbnGWt75M381s3zs3pXfaMdCmChbkaQdV3qoArSgrUrloUnyVOvAyMoOugjYwf+jX1qn/GD1N/YfHqE+meJy5OsvV/z6jd5hzTFt0jIjL+5OmqFS3Zs7YcCyeVpEA+dXRJRqHeghUlhezLFcTXozMdXf/dyXDqokMEh0Yy0iV9OhmevhDMhLl3uHAt9M28ooWNGTPQlm/qqDHujEgVcEVJBWVK5GFrQifDTgN8WDE7vpPhotXxnQynfJ92nQzv/B3B1AX32HPo+Zt5VhZZGNa3MF3a5CNbVvVBO6NSBVxRUsnrToZOLhvoOmgjy6a3xsrShEWrjhMc8gq3Cc3JljX1Ove9DIrGzfNvPL0f/2t+vy42DOhRCGtL9e+d0am3ZkVJRfnzWLBleRdKFc9D7xG+fF4sFz8OqMf2fddSrZPhq6g43Nc9pFqLs/8q3s0dcnJ8mx1jBxVVxTuTUAVcUVJZDmtTNi7pSOXyhRgwdgemJlmZ+WNjDp34i04DfD65k6GUkl0HAqjX7jwT3e4SEhbfm7zilxZsX/kly6aVokhB49T8URQdpwq4oqQBC/P4ToYOtUrw48x9BLwIZ8nUbzl3+SHt+q7n+cvkdTI8dyWEVr0v03fkTe49jH8DKFzAiKXTPmfHyi+xL2eZFj+GouNUAVeUNGJinJXls1rTunFZZi49woWrj//TyTA40ec4ejqQAvbHaN79EmcuhgBgZmrA2EG2HP65Ii0ccqmjSzIxVcAVJQ1lNczC/AnN6d7ODvd1p9jz6w3WLeiAf0AY3/Zay1/3nn/08bOX3f/X7V6O+Tm5w55+XQpglE39+2Z26i9AUdKYgYFg8vcNGdSrBt7bL7Jyox8bFjvyKiqGVr29uHLjyQcfWzB//Ek3jerm4LfNFZk4vBg5rLOmV3RFx6l2soqSjtzXnWKi20HqVC3KaNd69BjmS0joK9bOb0/l8oXeuf/9h5G8CIym/BcWGqRVdMWH2smqLXBFSUd9O1dhztimHD19lx9n7mPt/PhOhk4u3hw6/m4nw8IFjFXxVj5IFXBFSWeOLb9i2bRWXLz26E0nw+K2Oekx9Gd27NedToaK7lMFXFE00LRBKda4/dPJcN745lQoa8N3o7exfqu2nQwV/aEKuKJopE7VYngvju9k2GXQRiYMc6Betc/4fsovLFmT/p0MFf2jCriiaKjSV/GdDGNj4+jo6sOg3jVo4VCaKQsPMXPpEa3jKTouyQVcCJFFCHFeCLEr4fZ4IcRDIcSFhK8maRdTUTKuMiXysGV5F8xMs9FpgA+d21SgQc3iLF59gpiYOK3jKTosOVvgg4Dr/5k3T0pZPuFrTyrmUpRMpVjhHGxb0ZX8eSzoOmgTf94JoORnuTA0VB+SlQ9L0l+HEKIg0BTwTNs4ipJ5ve5kWLJYLu4/DKSGva3WkRQdl9S3dzfge+C/n+dchRCXhBArhRDZ3/dAIYSzEMJPCOH37NmzFERVlIwvh7Upm5Z2wrV7NXp2eOe8DUX5l0QLuBCiGeAvpTz7n0VLgc+A8sBjYM77Hi+l9JBS2ksp7XPnzp3CuIqS8VmYGzHKtR6FC1hrHUXRcUnp+l4DaJGwk9IYsBRCrJNSdn59ByHEcmBXGmVUFEVR3iPRLXAp5SgpZUEppS3gCPwqpewshMj/1t1aAVfSKKOiKIryHim57tJMIUR5QAJ3gb6pEUhRFEVJmmQVcCnlYeBwwnSXNMijKIqiJJE6yFRRFEVPqQKuKIqip1QBVxRF0VOqgCuKouipdL2kmhDiGXAvjZ4+FxCQRs+dGlS+lFH5UkblSzktMxaRUr5zJmS6FvC0JITwe98143SFypcyKl/KqHwpp4sZ1RCKoiiKnlIFXFEURU9lpALuoXWARKh8KaPypYzKl3I6lzHDjIEriqJkNhlpC1xRFCVTUQVcURRFT+l1ARdCfCWEOCGEuCyE2CmEsEyYn00IsSph/kUhRF0dy5dVCLEmYf51IcQoHcvX6a2LVV8QQsQldJ7UmYwJy8olLLuasNxYV/IJIWyFEBFvvYbL0jvbx/K9tbywECJUCDFcl/IJISq/9dpdFEK00rF8DkKIswnzzwoh6muRDyml3n4BZ4A6CdM9gUkJ0y7AqoTpPMBZwECH8nUEfBKmTYlvx2urK/n+c58vgds6+Ds2BC4BXyXczglk0aF8tsAVrV63pP6Ogc3Az8BwXcqX8H9hmDCdH/B/fVtH8lUAbBKmywIPtXj99HoLHPgc+C1hej/QJmG6DHAQQErpDwQCWhyA/6F8EjATQhgCJkAUEJz+8T6Y721OgHe6JXrXhzI2BC5JKS8CSCmfSyljdSifrvhgPiHEt8Bt4Gr6x3rjvfmklOFSypiE+cbE/89o4UP5zkspHyXMvwoYCyGM0jucvhfwK0CLhOl2QKGE6YtASyGEoRCiKGD31jJdyOcLhBF/LdH7wGwp5Yv0j/fBfG/rgLYF/EMZSwJSCLFXCHFOCPG9Juk+/hoWFUKcF0IcEULUSv9owAfyCSHMgB+ACRrleu2Dr58QoooQ4ipwGej3VkHXiXxvaQOcl1K+SrdUCVJyRZ50IYQ4AOR7z6Ifif9Is0AIMQ7YQfyWLMBKoDTgR3zvleNAmvzyPzFfZSAWsAGyA0eFEAeklLd1JN/rx1YBwqWUaXq5vE/MaAjUBCoB4cBBIcRZKeVBHcn3GCgspXwuhLADtgkhvpBSpvonrU/MNwGYJ6UMFUKkdqTUyIeU8hTwhRCiNLBGCPGLlDJSV/IlPPYLYAbxnwjTnxbjNmk0VlUSOP2BZceBMrqSD1gMdHlr2Uqgva7ke2vePGC01r/bD7yGjsDqt5aNBUboSr73LDsM2OtKPuAo8fte7hI/xPgCcNWVfO9ZdkiXXr+E2wWBP4AaWmXS6yEUIUSehO8GwBhgWcJt04SPiAghHIAYKeU1XclH/LBJfRHPDKgK3NChfK/ntQN80jvX2z6ScS9QLuF3bQjUAXTmdyyEyC2EyJIwXQwoQfx4s07kk1LWklLayviLlbsBU6WUi3QlnxCiaMLvFSFEEeLHou/qUD5rYDcwSkp5LL1zvabXBRxwEkL8QXzxewSsSpifBzgnhLhO/DifVtfv/FC+xYA58eNrZ4g/YuaSDuUDqA08kGkwrJNM780opXwJzCX+9bsAnJNS7taVfMS/fpeEEBeJ3+fRT2qzn+Njv2Nd8KF8NYGLQogLwFbgOymlFq1cP5TPFSgOjH3rcMc86R1OnUqvKIqip/R9C1xRFCXTUgVcURRFT6kCriiKoqdUAVcURdFTqoAriqLoKVXAFUVR9JQq4IqiKHrq/xRCYL5T2ulJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 3954, giving up w pop 647546.120128732\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3955 0 278535.71378667053 2.7802\n",
      "I am working on tract number 3960 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3969 10.0 1 84.1 1.1057 337142.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3971 2 222277.03899634146 0.9107\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3971 2 32948.5221465893 0.4554\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3971 2 222447.18094525635 0.9119\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3971 2 94299.7407941524 0.6836\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3971 2 146124.13728069304 0.7978\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3971 2 213246.52109338122 0.8549\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3971 2 182927.6235441895 0.8263\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3971 2 157538.4183943257 0.8121\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3971 2 168706.5082170937 0.8192\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3974 5.0 0 90.0 3.392 200256.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3976 3 223565.87161718175 3.8667\n",
      "I am working on tract number 3980 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3999 8.0 0 108.7 3.2405 202081.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3999 9.0 0 108.7 3.1515 202081.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3999 10.0 0 108.7 3.065 202081.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3999 0 202081.90981584077 2.9944\n",
      "I am working on tract number 4000 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 8.0 0 105.9 3.2806 200506.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 9.0 0 105.9 3.2317 200506.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 10.0 0 105.9 3.1836 200506.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 11.0 0 105.9 3.1362 200506.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 12.0 0 105.9 3.0895 200506.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 13.0 0 105.9 3.0435 200506.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 14.0 0 105.9 2.9981 200506.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 19.0 0 105.9 3.0177 200506.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 24.0 0 105.9 3.034 200506.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4003 25.0 0 105.9 2.9888 200506.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4003 0 200506.16502818733 3.0323\n",
      "loop31.0, tr4003,wedgePops676602.75, 71202.5, 123673.0, 253290.3, 228437.0, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 259215.9,-1.5161, 259215.9,0.6398, 259215.9,0.0634, 259215.9,0.1438\n",
      "loop32.0, tr4003,wedgePops698263.27, 71202.5, 123673.0, 256924.6, 246463.1, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 259215.9,-1.5161, 259215.9,0.6398, 259215.9,0.0052, 259215.9,0.1024\n",
      "loop33.0, tr4003,wedgePops713055.78, 71202.5, 123673.0, 258845.5, 259334.7, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 259215.9,-1.5161, 259215.9,0.6398, 259215.9,0.0026, 259215.9,0.0555\n",
      "I am working on tract number 4020 of 4706 tracts\n",
      "I am working on tract number 4040 of 4706 tracts\n",
      "I am working on tract number 4060 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4072\n",
      "we have 2 non-opposing shorted wedges for tract no 4074\n",
      "I am working on tract number 4080 of 4706 tracts\n",
      "I am working on tract number 4100 of 4706 tracts\n",
      "I am working on tract number 4120 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4122\n",
      "we have 2 non-opposing shorted wedges for tract no 4126\n",
      "we have 2 non-opposing shorted wedges for tract no 4136\n",
      "we have 2 non-opposing shorted wedges for tract no 4137\n",
      "I am working on tract number 4140 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4142\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4150 2.0 2 90.0 1.3498 199344.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4150 3.0 2 90.0 1.2401 199344.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4150 4.0 2 90.0 1.1393 199344.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4150 5.0 2 90.0 1.0467 199344.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4150 6.0 2 90.0 0.9616 199344.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4150 7.0 2 90.0 0.8835 199344.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4150 8.0 2 90.0 0.8117 199344.6\n",
      "we have 2 non-opposing shorted wedges for tract no 4158\n",
      "I am working on tract number 4160 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4174 10.0 0 83.4 2.9792 201604.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4174 20.0 0 83.4 3.8799 201604.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4174 21.0 0 83.4 3.5336 201604.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4174 22.0 0 83.4 3.2182 201604.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4174 0 201604.6491284771 3.2795\n",
      "loop31.0, tr4174,wedgePops727443.25, 61206.6, 234748.4, 214356.8, 217131.5, Overedge?1, 0, 0, 0, ,Satisfied?0001,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 217366.9,-1.6397, 217366.9,0.091, 217366.9,0.0227, 217366.9,0.0026\n",
      "loop32.0, tr4174,wedgePops709453.43, 61206.6, 214892.0, 216223.3, 217131.5, Overedge?1, 0, 0, 0, ,Satisfied?0001,yoyo?0100 \n",
      "   targetWP, latest drx4 are tWP,dr, 217366.9,-1.6397, 217366.9,-0.0555, 217366.9,0.0116, 217366.9,0.0026\n",
      "loop33.0, tr4174,wedgePops711183.45, 61206.6, 215787.8, 217057.5, 217131.5, Overedge?1, 0, 0, 0, ,Satisfied?0001,yoyo?0200 \n",
      "   targetWP, latest drx4 are tWP,dr, 217366.9,-1.6397, 217366.9,0.0056, 217366.9,0.0056, 217366.9,0.0026\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4175 10.0 3 90.0 3.8554 203648.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4175 11.0 3 90.0 3.4839 203648.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4175 17.0 3 90.0 3.8551 203648.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4175 18.0 3 90.0 3.4837 203648.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4175 3 203647.95167501533 4.4835\n",
      "loop31.0, tr4175,wedgePops714280.88, 179375.7, 178113.1, 178437.6, 178354.5, Overedge?0, 0, 0, 0, ,Satisfied?0110,yoyo?3003 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0387, 178326.8,0.0135, 178326.8,0.0039, 178326.8,-0.0094\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 5.0 0 90.0 3.3421 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 6.0 0 90.0 3.183 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 12.0 0 90.0 4.2663 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 13.0 0 90.0 4.0632 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 14.0 0 90.0 3.8698 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 15.0 0 90.0 3.6856 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 16.0 0 90.0 3.5102 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 17.0 0 90.0 3.3431 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 18.0 0 90.0 3.184 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 24.0 0 90.0 4.5753 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 25.0 0 90.0 4.3575 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 26.0 0 90.0 4.1501 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 27.0 0 90.0 3.9525 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 28.0 0 90.0 3.7644 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 29.0 0 90.0 3.5852 190206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 30.0 0 90.0 3.4146 190206.5\n",
      "loop31.0, tr4177,wedgePops725292.69, 190206.5, 178457.9, 178079.2, 178549.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5145 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.1625, 178326.8,-0.0062, 178326.8,0.0009, 178326.8,-0.0006\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4177 31.0 0 90.0 3.2521 190206.5\n",
      "loop32.0, tr4177,wedgePops725261.98, 190175.8, 178457.9, 178079.2, 178549.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5145 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.1548, 178326.8,-0.0062, 178326.8,0.0009, 178326.8,-0.0006\n",
      "loop33.0, tr4177,wedgePops586130.02, 51043.8, 178457.9, 178079.2, 178549.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5145 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-1.5486, 178326.8,-0.0062, 178326.8,0.0009, 178326.8,-0.0006\n",
      "loop34.0, tr4177,wedgePops677389.95, 142303.8, 178457.9, 178079.2, 178549.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6145 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.1585, 178326.8,-0.0062, 178326.8,0.0009, 178326.8,-0.0006\n",
      "loop35.0, tr4177,wedgePops684824.09, 149737.9, 178457.9, 178079.2, 178549.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6145 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.2706, 178326.8,-0.0062, 178326.8,0.0009, 178326.8,-0.0006\n",
      "loop36.0, tr4177,wedgePops725292.69, 190206.5, 178457.9, 178079.2, 178549.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6145 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.6938, 178326.8,-0.0062, 178326.8,0.0009, 178326.8,-0.0006\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4177 0 190206.5040402757 3.6715\n",
      "loop37.0, tr4177,wedgePops45.25, 11.3, 11.3, 11.3, 11.3, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0048, 178326.8,0.0048, 178326.8,0.0048, 178326.8,0.0048\n",
      "loop38.0, tr4177,wedgePops65239.87, 6601.5, 13669.0, 36416.5, 8552.9, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.4812, 178326.8,0.4812, 178326.8,0.4812, 178326.8,0.4812\n",
      "loop39.0, tr4177,wedgePops803162.55, 261336.1, 53724.4, 83978.5, 404123.6, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.6337, 178326.8,1.0161, 178326.8,0.4717, 178326.8,1.3877\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.11969 6601.4505 261336.0909 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.50211 13669.0161 53724.3708 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.95772 36416.4799 83978.5322 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.8737 8552.9262 404123.5595 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr4177,wedgePops720081.3, 194515.3, 163123.8, 260578.2, 101864.1, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?1001 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.2859, 178326.8,1.1362, 178326.8,0.4386, 178326.8,-0.474\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.83383 261336.0909 194515.2519 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.63833 53724.3708 163123.7996 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.39636 83978.5322 260578.1789 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.39974 404123.5595 101864.0677 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 4177, giving up w pop 720081.2981028332\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4179 5.0 2 90.0 3.8245 193732.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4179 6.0 2 90.0 3.5917 193732.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4179 7.0 2 90.0 3.3731 193732.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4179 8.0 2 90.0 3.1677 193732.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4179 19.0 2 90.0 3.01 193732.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4179 2 193732.11480996828 3.3205\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4179 27.0 0 83.3 2.1252 252514.1\n",
      "loop31.0, tr4179,wedgePops709844.82, 79389.3, 211485.1, 209077.0, 209893.4, Overedge?1, 0, 0, 0, ,Satisfied?0100,yoyo?0201 \n",
      "   targetWP, latest drx4 are tWP,dr, 211306.0,-0.5089, 211306.0,0.0315, 211306.0,0.0379, 211306.0,0.0372\n",
      "I am working on tract number 4180 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4184 5.0 3 90.0 3.7661 197461.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4184 6.0 3 90.0 3.4854 197461.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4184 7.0 3 90.0 3.2256 197461.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4188 10.0 3 90.0 3.7212 208227.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4188 3 200485.3737346716 3.1353\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4188 3 106499.49558941192 1.5676\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4188 3 204298.0928426229 3.1991\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4188 3 139316.5636651262 2.3834\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4188 3 144455.70715889247 2.7912\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4188 3 148738.04840546983 2.9952\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4188 3 164140.99702385685 3.0971\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4188 3 202329.86566324357 3.1481\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4193 0 276097.49173743965 2.3884\n",
      "loop31.0, tr4193,wedgePops575834.05, 40659.1, 178305.5, 178141.8, 178727.7, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 224216.1,-1.1942, 224216.1,0.0039, 224216.1,0.0052, 224216.1,-0.0088\n",
      "loop32.0, tr4193,wedgePops746719.01, 40659.1, 231975.7, 252113.7, 221970.6, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 224216.1,-1.1942, 224216.1,0.1606, 224216.1,0.2296, 224216.1,0.2\n",
      "loop33.0, tr4193,wedgePops712222.09, 40659.1, 231154.4, 215503.5, 224905.1, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0111 \n",
      "   targetWP, latest drx4 are tWP,dr, 224216.1,-1.1942, 224216.1,-0.0181, 224216.1,-0.0666, 224216.1,0.0076\n",
      "I am working on tract number 4200 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4202 5.0 1 90.0 3.8199 196571.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4202 6.0 1 90.0 3.5475 196571.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4202 7.0 1 90.0 3.2945 196571.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4215 3 264459.8077654873 2.7414\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4215 3 36254.422233503865 1.3707\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4215 3 264934.22888160276 2.7574\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4215 3 127445.87315762413 2.0641\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4215 3 158280.18407963784 2.4108\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4215 3 217832.57528629887 2.5841\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4215 3 262083.86517956102 2.6708\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4215 3 258702.96130253602 2.6274\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4215 3 248555.3393196109 2.6058\n",
      "I am working on tract number 4220 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4226\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4227 2.0 3 90.0 1.2399 252542.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4227 3.0 3 90.0 0.9429 252542.3\n",
      "I am working on tract number 4240 of 4706 tracts\n",
      "I am working on tract number 4260 of 4706 tracts\n",
      "I am working on tract number 4280 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 5.0 3 90.0 4.1339 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 6.0 3 90.0 4.0378 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 7.0 3 90.0 3.9439 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 8.0 3 90.0 3.8522 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 9.0 3 90.0 3.7627 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 10.0 3 90.0 3.6752 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 11.0 3 90.0 3.5897 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 12.0 3 90.0 3.5063 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 13.0 3 90.0 3.4248 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 14.0 3 90.0 3.3451 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 15.0 3 90.0 3.2674 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 16.0 3 90.0 3.1914 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 17.0 3 90.0 3.1172 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 18.0 3 90.0 3.0447 183986.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4284 19.0 3 90.0 2.9739 183986.2\n",
      "loop31.0, tr4284,wedgePops718729.62, 178382.2, 177914.5, 178446.8, 183986.2, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?1236 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.113, 178326.8,0.0012, 178326.8,-0.0009, 178326.8,0.7495\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4284 3 183986.20191114678 3.4741\n",
      "loop32.0, tr4284,wedgePops36.75, 9.2, 9.2, 9.2, 9.2, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0102, 178326.8,0.0102, 178326.8,0.0102, 178326.8,0.0102\n",
      "loop33.0, tr4284,wedgePops221355.68, 34481.8, 24569.5, 114571.6, 47732.7, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.1236, 178326.8,1.1236, 178326.8,1.1236, 178326.8,1.1236\n",
      "loop34.0, tr4284,wedgePops929371.31, 161401.3, 156072.3, 194221.4, 417676.4, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.1557, 178326.8,1.5368, 178326.8,0.2246, 178326.8,0.8462\n",
      "loop35.0, tr4284,wedgePops688133.75, 179884.5, 163210.2, 188344.8, 156694.3, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.09, 178326.8,0.1434, 178326.8,-0.0334, 178326.8,-0.3932\n",
      "loop36.0, tr4284,wedgePops734325.93, 173325.3, 219005.1, 175746.2, 166249.4, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?1012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.006, 178326.8,0.2254, 178326.8,-0.0472, 178326.8,0.029\n",
      "loop37.0, tr4284,wedgePops749136.19, 176791.4, 193811.8, 178343.8, 200189.2, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?2122 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0036, 178326.8,-0.1301, 178326.8,0.0078, 178326.8,0.0287\n",
      "loop38.0, tr4284,wedgePops697921.8, 178438.6, 165550.4, 178343.8, 175589.0, Overedge?0, 0, 0, 0, ,Satisfied?0010,yoyo?2123 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0013, 178326.8,-0.0663, 178326.8,0.0078, 178326.8,-0.0148\n",
      "loop39.0, tr4284,wedgePops704036.68, 178438.6, 170228.9, 178343.8, 177025.5, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?2224 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0013, 178326.8,0.0241, 178326.8,0.0078, 178326.8,0.0013\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.3784 176791.4112 178438.5753 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.86705 165550.4446 170228.8897 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.28554 175746.1675 178343.7653 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.63095 175589.019 177025.4517 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr4284,wedgePops722597.11, 178438.6, 187654.5, 178343.8, 178160.3, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?2224 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.0013, 178326.8,0.0331, 178326.8,0.0078, 178326.8,0.001\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.3784 176791.4112 178438.5753 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.90011 170228.8897 187654.4533 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.28554 175746.1675 178343.7653 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.63191 177025.4517 178160.3134 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 4284, giving up w pop 722597.1072878822\n",
      "we have 2 non-opposing shorted wedges for tract no 4296\n",
      "I am working on tract number 4300 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4306 5.0 0 90.0 1.9096 207398.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4306 6.0 0 90.0 1.7013 207398.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4306 7.0 0 90.0 1.5157 207398.7\n",
      "we have 2 non-opposing shorted wedges for tract no 4312\n",
      "I am working on tract number 4320 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4328\n",
      "I am working on tract number 4340 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4349 4.0 2 90.0 1.6267 183826.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4349 5.0 2 90.0 1.59 183826.7\n",
      "we have 2 non-opposing shorted wedges for tract no 4352\n",
      "I am working on tract number 4360 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4365\n",
      "I am working on tract number 4380 of 4706 tracts\n",
      "I am working on tract number 4400 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4407 1 202650.53236170136 1.0114\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4407 1 76755.83221639371 0.5057\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4407 1 414490.38021550677 1.1893\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4407 1 112546.12005584984 0.8475\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4407 1 213763.63363959015 1.0184\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4407 1 133121.93327963876 0.933\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4407 1 157213.157382404 0.9757\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4407 1 179874.41620104024 0.9971\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4407 1 196881.82351820602 1.0077\n",
      "I am working on tract number 4420 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4423\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4427 3 197064.23271555197 1.9831\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4427 3 18883.481831320212 0.9915\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4427 3 200548.52314770728 2.0058\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4427 3 65437.07527338283 1.4987\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4427 3 87128.10772579865 1.7523\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4427 3 117141.86154179675 1.879\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4427 3 174689.14779186842 1.9424\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4427 3 195280.09916278714 1.9741\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4427 3 190558.74633010346 1.9583\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4427 3 184607.1262353379 1.9504\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4428 3.0 2 90.0 2.0728 199836.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4428 8.0 2 90.0 2.0881 199836.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4428 2 199836.4971927664 2.1164\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4429 0 269490.32401992776 2.7069\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4429 0 47499.63737326872 1.3534\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4429 0 269492.1237557351 2.7109\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4429 0 120015.78750847318 2.0322\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4429 0 269168.1977930034 2.3715\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4429 0 256737.06024598103 2.2019\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4429 0 211595.4507479946 2.117\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4429 0 156260.37548005846 2.0746\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4429 0 194542.3510429021 2.0958\n",
      "I am working on tract number 4440 of 4706 tracts\n",
      "I am working on tract number 4460 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4460 0 223886.93063424557 2.7749\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4460 0 70367.71315720651 1.3874\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4460 0 223887.18364182662 2.7749\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4460 0 112137.18985313698 2.0812\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4460 0 153131.27076366454 2.428\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4460 0 215753.72402137055 2.6015\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4461 5.0 1 90.0 3.631 192017.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4461 6.0 1 90.0 3.4332 192017.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4461 7.0 1 90.0 3.2463 192017.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4461 8.0 1 90.0 3.0695 192017.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4461 14.0 1 90.0 3.0155 192017.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4461 20.0 1 90.0 3.6375 192017.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4461 21.0 1 90.0 3.4394 192017.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4461 22.0 1 90.0 3.2521 192017.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4461 23.0 1 90.0 3.075 192017.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4461 1 192017.05385742776 3.0754\n",
      "loop31.0, tr4461,wedgePops782922.68, 295168.9, 146524.4, 173761.9, 167467.5, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.0738, 178326.8,1.1813, 178326.8,0.0581, 178326.8,0.097\n",
      "loop32.0, tr4461,wedgePops763814.06, 246779.9, 160001.6, 177345.2, 179687.3, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?1000 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.3417, 178326.8,0.2064, 178326.8,0.0155, 178326.8,0.0457\n",
      "loop33.0, tr4461,wedgePops631928.46, 60940.7, 214391.3, 177942.1, 178654.3, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?1001 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.4914, 178326.8,0.2063, 178326.8,0.0034, 178326.8,-0.004\n",
      "loop34.0, tr4461,wedgePops740419.68, 180397.1, 203426.2, 177942.1, 178654.3, Overedge?0, 0, 0, 0, ,Satisfied?0011,yoyo?2101 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.2601, 178326.8,-0.1081, 178326.8,0.0034, 178326.8,-0.004\n",
      "loop35.0, tr4461,wedgePops688356.25, 178940.1, 152819.7, 177942.1, 178654.3, Overedge?0, 0, 0, 0, ,Satisfied?0011,yoyo?3101 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0034, 178326.8,-0.2116, 178326.8,0.0034, 178326.8,-0.004\n",
      "loop36.0, tr4461,wedgePops692847.02, 178497.0, 157753.6, 177942.1, 178654.3, Overedge?0, 0, 0, 0, ,Satisfied?0011,yoyo?3201 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0011, 178326.8,0.087, 178326.8,0.0034, 178326.8,-0.004\n",
      "loop37.0, tr4461,wedgePops750598.29, 178497.0, 215504.9, 177942.1, 178654.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?3201 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0011, 178326.8,0.2687, 178326.8,0.0034, 178326.8,-0.004\n",
      "loop38.0, tr4461,wedgePops741957.0, 178497.0, 206863.6, 177942.1, 178654.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?3301 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0011, 178326.8,-0.136, 178326.8,0.0034, 178326.8,-0.004\n",
      "loop39.0, tr4461,wedgePops676483.8, 178497.0, 141390.4, 177942.1, 178654.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?3301 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0011, 178326.8,-0.404, 178326.8,0.0034, 178326.8,-0.004\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.83853 178940.0995 178496.9955 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.43225 206863.6088 141390.4005 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.41925 177345.2121 177942.0836 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.48091 179687.346 178654.3164 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr4461,wedgePops688144.97, 178497.0, 153051.6, 177942.1, 178654.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?3401 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.0011, 178326.8,0.1884, 178326.8,0.0034, 178326.8,-0.004\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.83853 178940.0995 178496.9955 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.6206 141390.4005 153051.5719 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.41925 177345.2121 177942.0836 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.48091 179687.346 178654.3164 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 4461, giving up w pop 688144.9674874069\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4465 0 187316.69262321 2.2866\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4465 0 37736.38703306834 1.1433\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4465 0 187720.46416125595 2.3195\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4465 0 92792.14888571796 1.7314\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4465 0 140314.6130085009 2.0254\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4465 0 143675.34892370665 2.1725\n",
      "we have 2 non-opposing shorted wedges for tract no 4468\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4470 9.0 3 90.0 3.492 191359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4470 10.0 3 90.0 3.3105 191359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4470 11.0 3 90.0 3.1384 191359.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4470 0 213192.73354526632 2.8352\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4470 0 78442.66989979349 1.4176\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4470 17.0 3 90.0 3.2211 191359.8\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4470 0 215260.67853251635 2.8724\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4470 0 133965.2282709987 2.145\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4470 0 152568.1360770794 2.5087\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4470 0 206801.00275757545 2.6906\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4470 3 190708.5781507179 2.9039\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4470 0 198225.21963101052 2.5997\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4470 3 43164.44193622883 1.4519\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4470 0 169377.34049999138 2.5542\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4470 3 191359.7844494218 3.1274\n",
      "loop31.0, tr4470,wedgePops808316.5, 274005.7, 177643.0, 178563.6, 178104.2, Overedge?0, 0, 0, 0, ,Satisfied?0011,yoyo?3012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.3306, 178326.8,0.0874, 178326.8,-0.0019, 178326.8,0.0025\n",
      "loop32.0, tr4470,wedgePops557499.71, 22938.2, 177893.7, 178563.6, 178104.2, Overedge?0, 0, 0, 0, ,Satisfied?0011,yoyo?3012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-1.1911, 178326.8,0.0416, 178326.8,-0.0019, 178326.8,0.0025\n",
      "loop33.0, tr4470,wedgePops600956.02, 66394.5, 177893.7, 178563.6, 178104.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.6547, 178326.8,0.0416, 178326.8,-0.0019, 178326.8,0.0025\n",
      "loop34.0, tr4470,wedgePops840995.19, 306433.6, 177893.7, 178563.6, 178104.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.8744, 178326.8,0.0416, 178326.8,-0.0019, 178326.8,0.0025\n",
      "loop35.0, tr4470,wedgePops806028.99, 271467.4, 177893.7, 178563.6, 178104.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-0.3411, 178326.8,0.0416, 178326.8,-0.0019, 178326.8,0.0025\n",
      "loop36.0, tr4470,wedgePops573916.73, 39355.2, 177893.7, 178563.6, 178104.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,-1.0631, 178326.8,0.0416, 178326.8,-0.0019, 178326.8,0.0025\n",
      "loop37.0, tr4470,wedgePops605255.4, 70693.8, 177893.7, 178563.6, 178104.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,0.5615, 178326.8,0.0416, 178326.8,-0.0019, 178326.8,0.0025\n",
      "loop38.0, tr4470,wedgePops840995.19, 306433.6, 177893.7, 178563.6, 178104.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6012 \n",
      "   targetWP, latest drx4 are tWP,dr, 178326.8,1.0033, 178326.8,0.0416, 178326.8,-0.0019, 178326.8,0.0025\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4470 0 306433.63279003225 2.972\n",
      "loop39.0, tr4470,wedgePops578663.93, 44102.4, 177893.7, 178563.6, 178104.2, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 223068.3,-1.486, 223068.3,0.0416, 223068.3,-0.0019, 223068.3,0.0025\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.486 306433.6328 44102.3759 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 4.08495 177642.9609 177893.7407 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.91591 179098.2333 178563.6321 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.64271 176657.1014 178104.1815 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr4470,wedgePops653391.08, 44102.4, 181479.5, 213788.8, 214020.4, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 243862.7,-1.486, 243862.7,1.6355, 243862.7,0.1224, 243862.7,0.06\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.486 306433.6328 44102.3759 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 5.72043 177893.7407 181479.5368 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.03829 178563.6321 213788.7571 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.70267 178104.1815 214020.4101 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 4470, giving up w pop 653391.0798560156\n",
      "I am working on tract number 4480 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4489 7.0 3 45.4 3.6591 324941.6\n",
      "we have 2 non-opposing shorted wedges for tract no 4491\n",
      "we have 2 non-opposing shorted wedges for tract no 4496\n",
      "I am working on tract number 4500 of 4706 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 9.0 1 38.8 3.8481 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 10.0 1 38.8 3.7368 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 11.0 1 38.8 3.6287 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 12.0 1 38.8 3.5237 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 13.0 1 38.8 3.4217 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 14.0 1 38.8 3.3227 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 15.0 1 38.8 3.2266 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 16.0 1 38.8 3.1332 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 17.0 1 38.8 3.0426 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4501 18.0 1 38.8 2.9546 185410.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4508 3.0 0 101.9 0.9528 182237.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4508 4.0 0 101.9 0.9374 182237.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4508 5.0 0 101.9 0.9222 182237.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4508 6.0 0 101.9 0.9073 182237.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4508 7.0 0 101.9 0.8926 182237.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4508 8.0 0 101.9 0.8782 182237.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4509 2.0 0 90.0 1.5974 197950.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4509 0 192465.4620277551 1.2212\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4509 0 5702.024206793954 0.6106\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4509 0 195717.68811647536 1.2598\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4509 0 27387.0262621766 0.9352\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4509 0 117282.57171597745 1.0975\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4509 0 163254.84268147362 1.1787\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4509 0 191968.73418283582 1.2192\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4509 0 184083.72127914464 1.199\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4509 0 172571.90342572916 1.1888\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4510 0 205970.07474703726 1.5303\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4510 0 13654.486950552702 0.7652\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4510 0 206844.019090207 1.5451\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4510 0 133248.13469568093 1.1552\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4510 0 154323.5275979361 1.3501\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4510 0 188202.8352237106 1.4476\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4510 0 158132.77568939002 1.3989\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4510 0 164517.17173549815 1.4233\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4510 0 174598.5965961748 1.4354\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4513 0 217717.1750333198 3.7333\n",
      "I am working on tract number 4520 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4520\n",
      "we have 2 non-opposing shorted wedges for tract no 4521\n",
      "we have 2 non-opposing shorted wedges for tract no 4522\n",
      "we have 2 non-opposing shorted wedges for tract no 4524\n",
      "we have 2 non-opposing shorted wedges for tract no 4525\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4525 9.0 1 89.4 5.7373 433397.3\n",
      "we have 2 non-opposing shorted wedges for tract no 4526\n",
      "we have 2 non-opposing shorted wedges for tract no 4527\n",
      "we have 2 non-opposing shorted wedges for tract no 4528\n",
      "we have 2 non-opposing shorted wedges for tract no 4529\n",
      "we have 2 non-opposing shorted wedges for tract no 4530\n",
      "I am working on tract number 4540 of 4706 tracts\n",
      "I am working on tract number 4560 of 4706 tracts\n",
      "I am working on tract number 4580 of 4706 tracts\n",
      "I am working on tract number 4600 of 4706 tracts\n",
      "I am working on tract number 4620 of 4706 tracts\n",
      "I am working on tract number 4640 of 4706 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4642 3 184998.90939465567 0.7687\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4642 3 24825.453740745084 0.3843\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4642 3 204314.8540464525 0.7766\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4642 3 68285.17417321575 0.5805\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4642 3 113063.0913669908 0.6785\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4642 3 133035.89334162482 0.7275\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4642 3 157239.3645881959 0.7521\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4642 3 173887.80476545615 0.7643\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4642 3 189845.62803454502 0.7704\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4642 3 181507.0247886158 0.7674\n",
      "I am working on tract number 4660 of 4706 tracts\n",
      "I am working on tract number 4680 of 4706 tracts\n",
      "I am working on tract number 4700 of 4706 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4704\n",
      "we have 2 non-opposing shorted wedges for tract no 4705\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0004464341585562\n",
      "Average and max number of wedgePop loops per tract were:  6.739269018274543 40.0\n",
      "min,average,max of Home District areas were:  0.0 2.4267365704203927 12.702280807783103\n",
      "calculated statewide vote was 0.46684516773018864, should have been 0.46360486351433333\n",
      "calcd statewide Hispanic pop was 0.04786577163973402, should have been 0.049834667454083846\n",
      "calcd statewide Black pop was 0.0564445023080213, should have been 0.0593349377869795\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.28665533361665957\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0004464341585562\n",
      "Average and max number of wedgePop loops per tract were:  6.739269018274543 40.0\n",
      "min,average,max of Home District areas were:  0.0 2.4267365704203927 12.702280807783103 for  MN\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start MN-bg 4:20pm end 11:12am\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MN 19AugBG\n"
     ]
    }
   ],
   "source": [
    "date = \"19AugBG\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "788 1.01 ( -92.98752345883096 43.98580247941786 ) 0.5565662215763378 t, pop/target,(x,y), pctR\n",
      "839 0.87 ( -93.16843879178312 45.33790629269456 ) 0.5609561969753313 t, pop/target,(x,y), pctR\n",
      "1111 1.02 ( -93.06104753046088 45.27395577494856 ) 0.47527506326602553 t, pop/target,(x,y), pctR\n",
      "1361 1.01 ( -94.1642950323451 43.75840441131438 ) 0.5631698525377711 t, pop/target,(x,y), pctR\n",
      "1650 0.95 ( -93.57031020185725 46.58155228465246 ) 0.5717873217477952 t, pop/target,(x,y), pctR\n",
      "2071 0.98 ( -93.01010797995174 44.9598955784138 ) 0.39142716809159667 t, pop/target,(x,y), pctR\n",
      "2898 1.01 ( -93.01496624603962 45.068385069976024 ) 0.4184432724702357 t, pop/target,(x,y), pctR\n",
      "3247 1.01 ( -93.16074133809386 45.242344901637495 ) 0.5230194254228965 t, pop/target,(x,y), pctR\n",
      "3574 1.0 ( -92.97801210908425 44.9750463145858 ) 0.42926288403318 t, pop/target,(x,y), pctR\n",
      "3954 0.91 ( -94.14414929672097 46.84888917305345 ) 0.6435301890821585 t, pop/target,(x,y), pctR\n",
      "4177 1.01 ( -93.94332566240962 46.51283212102278 ) 0.5944300649559291 t, pop/target,(x,y), pctR\n",
      "4284 1.01 ( -94.07595800444754 46.70087426108721 ) 0.5817577537881454 t, pop/target,(x,y), pctR\n",
      "4461 0.96 ( -94.06728830427373 46.63716202016937 ) 0.5938496099904735 t, pop/target,(x,y), pctR\n",
      "4470 0.92 ( -94.43110041350754 46.81113683572048 ) 0.6566547309796348 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [839, 3954, 4470]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.95 or ratio > 1.05) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f8a3747-b71b-4f42-9824-c9fd63a5415f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#decision for MN -- only 3 of >4000 block groups that are more than 5% off in pop -- do not do any redo's for fear of overwriting good data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 0.784392278413743 117 pop,GOP 0.7350427350427351\n",
      "16 0.6614802051080739 81 pop,GOP 0.6790123456790124\n",
      "22 0.7283748904384728 86 pop,GOP 0.6395348837209303\n",
      "25 0.7760677963411902 78 pop,GOP 0.6794871794871795\n",
      "34 0.7572601579752093 13 pop,GOP 0.6153846153846154\n",
      "35 0.7852433000619757 224 pop,GOP 0.7366071428571429\n",
      "44 0.7485380317843476 43 pop,GOP 0.7441860465116279\n",
      "49 0.7095530295501402 92 pop,GOP 0.8804347826086957\n",
      "55 0.6345139099839283 10 pop,GOP 0.6\n",
      "60 0.6419468370229117 100 pop,GOP 0.82\n",
      "65 0.6639485659208302 363 pop,GOP 0.7272727272727273\n",
      "66 0.6858572033419431 9 pop,GOP 1.0\n",
      "74 0.7223923459802369 43 pop,GOP 0.7441860465116279\n",
      "80 0.7726849385180135 132 pop,GOP 0.7348484848484849\n",
      "88 0.7896537223668754 64 pop,GOP 0.59375\n",
      "94 0.7404806592170946 30 pop,GOP 0.8\n",
      "95 0.6541932969591088 23 pop,GOP 0.782608695652174\n",
      "107 0.7163466733180923 7883 pop,GOP 0.49562349359380947\n",
      "110 0.7708418807191167 62 pop,GOP 0.7580645161290323\n",
      "111 0.7655751475289777 65 pop,GOP 0.7384615384615385\n",
      "117 0.6789924799635912 62 pop,GOP 0.9032258064516129\n",
      "131 0.7900407059775886 74 pop,GOP 0.7972972972972973\n",
      "133 0.7306475475908502 7912 pop,GOP 0.49785136501516686\n",
      "135 0.7000012627186789 94 pop,GOP 0.6808510638297872\n",
      "143 0.7582315638453617 24 pop,GOP 0.5833333333333334\n",
      "144 0.7629756643270471 45 pop,GOP 0.8888888888888888\n",
      "145 0.7435137799960218 30 pop,GOP 0.8\n",
      "154 0.6920165972705987 43 pop,GOP 0.6744186046511628\n",
      "155 0.7827907623944075 170 pop,GOP 0.7\n",
      "164 0.7552970865707678 322 pop,GOP 0.6739130434782609\n",
      "167 0.6084952549509205 289 pop,GOP 0.7612456747404844\n",
      "177 0.7502727057239893 21 pop,GOP 0.47619047619047616\n",
      "180 0.7912075551435266 48 pop,GOP 0.5625\n",
      "194 0.7905837387451069 58 pop,GOP 0.6551724137931034\n",
      "202 0.7862852998411611 131 pop,GOP 0.5801526717557252\n",
      "213 0.7959880965501894 148 pop,GOP 0.75\n",
      "222 0.7595976060397772 104 pop,GOP 0.7692307692307693\n",
      "225 0.7699855951282296 44 pop,GOP 0.6136363636363636\n",
      "247 0.7812823751964906 406 pop,GOP 0.05172413793103448\n",
      "248 0.6363916049394738 28 pop,GOP 0.5714285714285714\n",
      "252 0.7801100029636416 39 pop,GOP 0.7948717948717948\n",
      "261 0.6492008493680779 344 pop,GOP 0.7732558139534884\n",
      "262 0.763651387254202 140 pop,GOP 0.5714285714285714\n",
      "264 0.6441832111969055 23 pop,GOP 0.30434782608695654\n",
      "270 0.6572223517742459 4598 pop,GOP 0.4164854284471509\n",
      "272 0.7783790206692444 44 pop,GOP 0.5227272727272727\n",
      "273 0.7734657917515098 173 pop,GOP 0.861271676300578\n",
      "275 0.7423607108964626 47 pop,GOP 0.8936170212765957\n",
      "277 0.6772985981691719 24 pop,GOP 0.7916666666666666\n",
      "283 0.7046471865803245 127 pop,GOP 0.7480314960629921\n",
      "285 0.6763952534747031 103 pop,GOP 0.7184466019417476\n",
      "286 0.787606700935128 37 pop,GOP 0.6216216216216216\n",
      "289 0.7030563608991284 24 pop,GOP 0.5\n",
      "290 0.7480244485830352 95 pop,GOP 0.8210526315789474\n",
      "298 0.7304420723384797 55 pop,GOP 0.6363636363636364\n",
      "302 0.6576058803663953 18 pop,GOP 0.8333333333333334\n",
      "305 0.669416654590579 32 pop,GOP 0.6875\n",
      "325 0.752056298577024 17 pop,GOP 0.7647058823529411\n",
      "326 0.6652571865711291 120 pop,GOP 0.7916666666666666\n",
      "328 0.731873451764174 23 pop,GOP 0.8695652173913043\n",
      "329 0.7321257528853777 27 pop,GOP 0.8148148148148148\n",
      "337 0.7301318276290945 83 pop,GOP 0.6626506024096386\n",
      "338 0.6659096728657582 52 pop,GOP 0.8461538461538461\n",
      "342 0.7696907347489428 141 pop,GOP 0.6099290780141844\n",
      "343 0.7810830726731969 165 pop,GOP 0.5333333333333333\n",
      "349 0.7492317063827232 332 pop,GOP 0.6837349397590361\n",
      "351 0.6995249612188316 80 pop,GOP 0.75\n",
      "356 0.6838034969078507 29 pop,GOP 0.8620689655172413\n",
      "366 0.7771134926865255 359 pop,GOP 0.6768802228412256\n",
      "381 0.768874629159125 31 pop,GOP 0.967741935483871\n",
      "389 0.6164878097505649 10 pop,GOP 0.8\n",
      "406 0.7058426236955443 44 pop,GOP 0.75\n",
      "410 0.1848355561102428 4881 pop,GOP 0.4187666461790617\n",
      "412 0.7536720930101429 23 pop,GOP 0.8260869565217391\n",
      "413 0.6771999975352089 61 pop,GOP 0.639344262295082\n",
      "418 0.7521725826983172 36 pop,GOP 0.75\n",
      "433 0.7299332003768878 37 pop,GOP 0.7567567567567568\n",
      "434 0.7025529356145466 149 pop,GOP 0.610738255033557\n",
      "436 0.7815401697131071 203 pop,GOP 0.625615763546798\n",
      "437 0.7791986277350486 21 pop,GOP 0.6190476190476191\n",
      "442 0.7661094191439263 60 pop,GOP 0.6166666666666667\n",
      "448 0.7973308747241107 88 pop,GOP 0.8636363636363636\n",
      "459 0.6370718908208285 60 pop,GOP 0.7166666666666667\n",
      "470 0.6409416845258193 8 pop,GOP 0.75\n",
      "480 0.7466052601264875 873 pop,GOP 0.6391752577319587\n",
      "493 0.703870138625627 58 pop,GOP 0.5172413793103449\n",
      "495 0.7902733108460933 90 pop,GOP 0.6444444444444445\n",
      "501 0.7484373231277707 123 pop,GOP 0.6422764227642277\n",
      "508 0.7445657199324932 117 pop,GOP 0.5470085470085471\n",
      "510 0.7974634313978448 344 pop,GOP 0.5494186046511628\n",
      "511 0.7554309751410765 63 pop,GOP 0.9047619047619048\n",
      "512 0.715928873372132 15 pop,GOP 0.4\n",
      "528 0.6818193876149399 59 pop,GOP 0.847457627118644\n",
      "535 0.6528098795678774 213 pop,GOP 0.6525821596244131\n",
      "538 0.6757748364837153 87 pop,GOP 0.8620689655172413\n",
      "539 0.7812375791268363 84 pop,GOP 0.7857142857142857\n",
      "541 0.7695245743387708 36 pop,GOP 0.6388888888888888\n",
      "545 0.6772385272146237 30 pop,GOP 0.7\n",
      "556 0.6513032378133401 26 pop,GOP 0.5384615384615384\n",
      "557 0.792766851741923 57 pop,GOP 0.5614035087719298\n",
      "558 0.788553067226651 102 pop,GOP 0.6078431372549019\n",
      "560 0.6694114880209756 159 pop,GOP 0.6415094339622641\n",
      "574 0.7786400290217852 86 pop,GOP 0.7441860465116279\n",
      "576 0.7390701573660767 72 pop,GOP 0.7638888888888888\n",
      "596 0.7776547536538087 31 pop,GOP 0.5483870967741935\n",
      "598 0.7284719527673167 98 pop,GOP 0.7040816326530612\n",
      "601 0.7897382899674004 62 pop,GOP 0.532258064516129\n",
      "627 0.0009396030558524165 200 pop,GOP 0.71\n",
      "630 0.7028394198408038 38 pop,GOP 0.7894736842105263\n",
      "635 0.7167025500773822 108 pop,GOP 0.5740740740740741\n",
      "640 0.7593797789886151 215 pop,GOP 0.6651162790697674\n",
      "652 0.748830418118135 101 pop,GOP 0.6732673267326733\n",
      "662 0.7393934259016349 43 pop,GOP 0.627906976744186\n",
      "670 0.7090767024602075 11230 pop,GOP 0.5153161175422974\n",
      "675 0.6762847791183575 25 pop,GOP 0.84\n",
      "687 0.7451224727462739 63 pop,GOP 0.746031746031746\n",
      "688 0.6661096531671321 51 pop,GOP 0.9019607843137255\n",
      "689 0.7666460143881041 41 pop,GOP 0.7804878048780488\n",
      "694 0.6807186408237894 27 pop,GOP 0.7407407407407407\n",
      "707 0.779727836363296 206 pop,GOP 0.6213592233009708\n",
      "709 0.7134277670177845 118 pop,GOP 0.8305084745762712\n",
      "710 0.7714988293938322 82 pop,GOP 0.8414634146341463\n",
      "718 0.7809119879109953 432 pop,GOP 0.6319444444444444\n",
      "721 0.6539428646150611 62 pop,GOP 0.7580645161290323\n",
      "733 0.7784472921312202 337 pop,GOP 0.5014836795252225\n",
      "748 0.7384231847675078 517 pop,GOP 0.5493230174081238\n",
      "766 0.6731806533818816 342 pop,GOP 0.7777777777777778\n",
      "768 0.7303645914560585 11 pop,GOP 0.45454545454545453\n",
      "769 0.5590407304016098 225 pop,GOP 0.68\n",
      "772 0.7950322720139704 539 pop,GOP 0.5565862708719852\n",
      "779 0.7458920134591541 428 pop,GOP 0.5700934579439252\n",
      "781 0.6674112457817941 131 pop,GOP 0.7022900763358778\n",
      "782 0.7715671951380096 36 pop,GOP 0.6944444444444444\n",
      "783 0.6669048411670302 55 pop,GOP 0.8181818181818182\n",
      "787 0.6939330835498022 27 pop,GOP 0.6296296296296297\n",
      "795 0.7742653025138397 37 pop,GOP 0.7567567567567568\n",
      "799 0.7973897180989643 168 pop,GOP 0.6726190476190477\n",
      "805 0.7265011446348526 2163 pop,GOP 0.47619047619047616\n",
      "823 0.7402711081497525 408 pop,GOP 0.024509803921568627\n",
      "847 0.6968728845804445 6811 pop,GOP 0.42548818088386436\n",
      "855 0.7899215423541303 146 pop,GOP 0.7808219178082192\n",
      "857 0.6282060812652203 32 pop,GOP 0.3125\n",
      "859 0.7021847930056154 14 pop,GOP 0.7142857142857143\n",
      "869 0.6571218486326271 32 pop,GOP 0.875\n",
      "886 0.7465021870247729 32 pop,GOP 0.9375\n",
      "890 0.7667273271458807 32 pop,GOP 0.65625\n",
      "893 0.6493486930984701 223 pop,GOP 0.8116591928251121\n",
      "894 0.7839429011231857 58 pop,GOP 0.7068965517241379\n",
      "903 0.7520475189229207 62 pop,GOP 0.6935483870967742\n",
      "904 0.6857686183180456 251 pop,GOP 0.7569721115537849\n",
      "913 0.7445586335670125 161 pop,GOP 0.6273291925465838\n",
      "918 0.7348055989562029 80 pop,GOP 0.825\n",
      "920 0.7798113196050632 179 pop,GOP 0.553072625698324\n",
      "926 0.7259224161220423 632 pop,GOP 0.6613924050632911\n",
      "928 0.7577335521941176 178 pop,GOP 0.5786516853932584\n",
      "931 0.6915685168502403 19 pop,GOP 1.0\n",
      "934 0.7192304091750662 24 pop,GOP 0.9166666666666666\n",
      "939 0.009848303196418768 530 pop,GOP 0.5716981132075472\n",
      "946 0.714306365936231 29 pop,GOP 0.7241379310344828\n",
      "953 0.7454981897237545 79 pop,GOP 0.4050632911392405\n",
      "955 0.6526497592033027 19 pop,GOP 1.0\n",
      "962 0.6237361244539739 67 pop,GOP 0.7164179104477612\n",
      "969 0.7393453987589728 110 pop,GOP 0.7545454545454545\n",
      "970 0.7534327198846075 358 pop,GOP 0.7094972067039106\n",
      "972 0.787028315426495 38 pop,GOP 0.6578947368421053\n",
      "996 0.7656151931012357 218 pop,GOP 0.8807339449541285\n",
      "997 0.7628589959065748 142 pop,GOP 0.7676056338028169\n",
      "998 0.660079193793342 117 pop,GOP 0.8205128205128205\n",
      "1000 0.7284868524819536 57 pop,GOP 0.8421052631578947\n",
      "1004 0.7236234928928862 38 pop,GOP 0.8421052631578947\n",
      "1022 0.7935875712325702 129 pop,GOP 0.6821705426356589\n",
      "1032 0.792550648370184 178 pop,GOP 0.8033707865168539\n",
      "1034 0.7921621061826821 150 pop,GOP 0.78\n",
      "1039 0.7851879164264894 117 pop,GOP 0.7606837606837606\n",
      "1043 0.6973549368490765 55 pop,GOP 0.7636363636363637\n",
      "1049 0.7968615281718391 385 pop,GOP 0.5844155844155844\n",
      "1052 0.78277032932202 68 pop,GOP 0.6176470588235294\n",
      "1053 0.7654864878556069 202 pop,GOP 0.7079207920792079\n",
      "1069 0.7518091159435015 55 pop,GOP 0.7454545454545455\n",
      "1071 0.7797289133764429 415 pop,GOP 0.6168674698795181\n",
      "1072 0.7087205555027548 47 pop,GOP 0.7446808510638298\n",
      "1079 0.6234284399336165 70 pop,GOP 0.6\n",
      "1081 0.7993949082372176 441 pop,GOP 0.5918367346938775\n",
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      "1087 0.7997288327550929 101 pop,GOP 0.7920792079207921\n",
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      "1106 0.6960731129908965 55 pop,GOP 0.7636363636363637\n",
      "1119 0.7838055349385689 68 pop,GOP 0.8235294117647058\n",
      "1123 0.619186258746621 23 pop,GOP 0.5217391304347826\n",
      "1127 0.7224859924605643 169 pop,GOP 0.6982248520710059\n",
      "1130 0.6772585415188689 44 pop,GOP 0.6818181818181818\n",
      "1140 0.7712435571550985 16 pop,GOP 0.875\n",
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      "1146 0.7697165346462254 233 pop,GOP 0.648068669527897\n",
      "1153 0.6992364782322739 75 pop,GOP 0.8533333333333334\n",
      "1155 0.6766429382777125 67 pop,GOP 0.7313432835820896\n",
      "1159 0.717469184226309 50 pop,GOP 0.54\n",
      "1163 0.6721789666524907 79 pop,GOP 0.7088607594936709\n",
      "1166 0.643889249430173 20 pop,GOP 0.85\n",
      "1174 0.7523416037537001 64 pop,GOP 0.546875\n",
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      "1180 0.7068016278187713 83 pop,GOP 0.6024096385542169\n",
      "1184 0.7543584708534937 23 pop,GOP 0.43478260869565216\n",
      "1191 0.7414256263609965 279 pop,GOP 0.7347670250896058\n",
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      "1217 0.729517967849366 40 pop,GOP 0.75\n",
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      "1221 0.7256860087873851 75 pop,GOP 0.6666666666666666\n",
      "1222 0.7780467217216849 8142 pop,GOP 0.5023335789732253\n",
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      "1224 0.7565249784702747 211 pop,GOP 0.7014218009478673\n",
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      "1260 0.6066009456065251 71 pop,GOP 0.6901408450704225\n",
      "1262 0.7741274109744835 53 pop,GOP 0.6981132075471698\n",
      "1269 0.7599173845977006 53 pop,GOP 0.7547169811320755\n",
      "1275 0.687962491846601 70 pop,GOP 0.7428571428571429\n",
      "1276 0.7063495780986678 73 pop,GOP 0.7534246575342466\n",
      "1278 0.6567053883308276 18 pop,GOP 0.5555555555555556\n",
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      "1290 0.6624528821456779 7912 pop,GOP 0.49519716885743176\n",
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      "1296 0.7525179186673411 77 pop,GOP 0.6233766233766234\n",
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      "1312 0.7068659465889726 27 pop,GOP 0.6296296296296297\n",
      "1322 0.6496079676078101 11 pop,GOP 0.9090909090909091\n",
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      "1330 0.79013175485644 181 pop,GOP 0.5193370165745856\n",
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      "1366 0.6466810862676742 17 pop,GOP 0.7647058823529411\n",
      "1377 0.6290952457611084 72 pop,GOP 0.875\n",
      "1387 0.656128296481724 103 pop,GOP 0.7087378640776699\n",
      "1396 0.7743809813786691 69 pop,GOP 0.782608695652174\n",
      "1400 0.7421008726649542 54 pop,GOP 0.7962962962962963\n",
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      "1410 0.6846274508656743 73 pop,GOP 0.821917808219178\n",
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      "1414 0.7972852478648744 204 pop,GOP 0.6470588235294118\n",
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      "1431 0.7932065624331514 50 pop,GOP 0.48\n",
      "1436 0.7806494602986792 128 pop,GOP 0.6875\n",
      "1437 0.722473621735883 220 pop,GOP 0.5681818181818182\n",
      "1443 0.6878953703796058 81 pop,GOP 0.5802469135802469\n",
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      "1459 0.6718330447729283 47 pop,GOP 0.851063829787234\n",
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      "1468 0.6711156989337104 7780 pop,GOP 0.49241645244215937\n",
      "1471 0.7736206385777472 64 pop,GOP 0.6875\n",
      "1488 0.7934918922107151 258 pop,GOP 0.5891472868217055\n",
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      "1506 0.690767358914176 70 pop,GOP 0.7857142857142857\n",
      "1511 0.7447133949319578 74 pop,GOP 0.6351351351351351\n",
      "1518 0.7839945239073682 46 pop,GOP 0.5869565217391305\n",
      "1520 0.7826370574522556 85 pop,GOP 0.7647058823529411\n",
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      "1535 0.771353569466905 31 pop,GOP 0.6129032258064516\n",
      "1549 0.6500700721273426 31 pop,GOP 0.8709677419354839\n",
      "1550 0.7938031494654509 69 pop,GOP 0.7246376811594203\n",
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      "1554 0.7622755246592575 51 pop,GOP 0.6470588235294118\n",
      "1555 0.793035996800153 26 pop,GOP 1.0\n",
      "1563 0.7386797428551805 66 pop,GOP 0.803030303030303\n",
      "1567 0.7695844023894213 612 pop,GOP 0.44607843137254904\n",
      "1572 0.7734311150032005 86 pop,GOP 0.5697674418604651\n",
      "1577 0.7161314694292917 97 pop,GOP 0.7525773195876289\n",
      "1583 0.7852150242679309 73 pop,GOP 0.863013698630137\n",
      "1593 0.6270894062903367 20 pop,GOP 0.55\n",
      "1597 0.736116767838797 235 pop,GOP 0.7404255319148936\n",
      "1604 0.7759975230670946 35 pop,GOP 0.7714285714285715\n",
      "1608 0.7700329488024283 34 pop,GOP 0.7647058823529411\n",
      "1612 0.736402971078157 32 pop,GOP 0.875\n",
      "1620 0.7617908058219633 48 pop,GOP 0.4791666666666667\n",
      "1626 0.6498677486199557 13 pop,GOP 1.0\n",
      "1632 0.7406640610067616 54 pop,GOP 0.5\n",
      "1637 0.7971452932969192 82 pop,GOP 0.6097560975609756\n",
      "1647 0.7665690299772132 61 pop,GOP 0.5081967213114754\n",
      "1648 0.7696372053987974 34 pop,GOP 0.8235294117647058\n",
      "1656 0.7237050787855608 163 pop,GOP 0.7423312883435583\n",
      "1664 0.7237911109658056 39 pop,GOP 0.7435897435897436\n",
      "1677 0.615804695270247 36 pop,GOP 0.6111111111111112\n",
      "1680 0.6248525583531909 932 pop,GOP 0.7746781115879828\n",
      "1698 0.6160671001082232 79 pop,GOP 0.8481012658227848\n",
      "1703 0.7117846745831111 119 pop,GOP 0.6638655462184874\n",
      "1704 0.6327746817209976 117 pop,GOP 0.358974358974359\n",
      "1705 0.7481663456640254 29 pop,GOP 0.7241379310344828\n",
      "1707 0.7429538926213866 276 pop,GOP 0.5108695652173914\n",
      "1716 0.7823272696524053 351 pop,GOP 0.6353276353276354\n",
      "1722 0.7931700348786367 98 pop,GOP 0.7653061224489796\n",
      "1725 0.7550777981357011 106 pop,GOP 0.6886792452830188\n",
      "1727 0.7583273165665494 40 pop,GOP 0.75\n",
      "1728 0.7903457225234074 56 pop,GOP 0.6607142857142857\n",
      "1734 0.7963562157036786 146 pop,GOP 0.815068493150685\n",
      "1736 0.7471298623463618 43 pop,GOP 0.627906976744186\n",
      "1737 0.7751667352331513 65 pop,GOP 0.6\n",
      "1746 0.7564862390363205 35 pop,GOP 0.7714285714285715\n",
      "1766 0.6287162336340132 440 pop,GOP 0.725\n",
      "1767 0.7079975968952996 89 pop,GOP 0.7191011235955056\n",
      "1771 0.632899981210483 21 pop,GOP 1.0\n",
      "1774 0.7836552818511903 169 pop,GOP 0.6745562130177515\n",
      "1782 0.6592665147696243 85 pop,GOP 0.6941176470588235\n",
      "1788 0.7955421396081409 192 pop,GOP 0.6979166666666666\n",
      "1796 0.7397622621529103 116 pop,GOP 0.7068965517241379\n",
      "1799 0.760095223160423 31 pop,GOP 0.7741935483870968\n",
      "1809 0.754610267808228 52 pop,GOP 0.8076923076923077\n",
      "1811 0.7660374639027665 25 pop,GOP 0.72\n",
      "1815 0.7138209087563282 39 pop,GOP 0.7435897435897436\n",
      "1821 0.7110462365826372 28 pop,GOP 0.75\n",
      "1824 0.7578765008499375 15 pop,GOP 0.6666666666666666\n",
      "1831 0.6884970175299334 139 pop,GOP 0.4748201438848921\n",
      "1832 0.6641336167031516 444 pop,GOP 0.6981981981981982\n",
      "1836 0.7951763059267494 74 pop,GOP 0.8513513513513513\n",
      "1845 0.7337496284553519 27 pop,GOP 0.7777777777777778\n",
      "1847 0.6917232575083573 37 pop,GOP 0.5945945945945946\n",
      "1848 0.6232598209433677 14 pop,GOP 0.42857142857142855\n",
      "1851 0.663487738019724 176 pop,GOP 0.6988636363636364\n",
      "1853 0.7321213284619759 103 pop,GOP 0.9514563106796117\n",
      "1867 0.7567193340779265 72 pop,GOP 0.7361111111111112\n",
      "1872 7.973283027320338e-05 461 pop,GOP 0.5097613882863341\n",
      "1876 0.6915135255181958 105 pop,GOP 0.6476190476190476\n",
      "1883 0.6290512266567615 201 pop,GOP 0.6616915422885572\n",
      "1894 0.6590612880863485 188 pop,GOP 0.48404255319148937\n",
      "1899 0.75139054042906 6 pop,GOP 0.6666666666666666\n",
      "1908 0.6612181187039109 59 pop,GOP 0.7627118644067796\n",
      "1909 0.05490196942182802 116 pop,GOP 0.7586206896551724\n",
      "1910 0.5917340554052567 334 pop,GOP 0.7574850299401198\n",
      "1915 0.6752908953267857 101 pop,GOP 0.8217821782178217\n",
      "1917 0.6955436531835656 27 pop,GOP 0.7037037037037037\n",
      "1922 0.7629221239383203 19 pop,GOP 0.8947368421052632\n",
      "1924 0.7135008464645506 64 pop,GOP 0.875\n",
      "1929 0.6870110862539033 56 pop,GOP 0.7321428571428571\n",
      "1931 0.5595920577116885 5001 pop,GOP 0.41451709658068386\n",
      "1948 0.7765288483389444 30 pop,GOP 0.5\n",
      "1950 0.7411615388805116 17 pop,GOP 0.6470588235294118\n",
      "1958 0.7708730974221977 275 pop,GOP 0.72\n",
      "1974 0.6870060292231475 916 pop,GOP 0.6037117903930131\n",
      "1975 0.646586338449394 29 pop,GOP 0.7241379310344828\n",
      "1977 0.7294807179354903 138 pop,GOP 0.5942028985507246\n",
      "1986 0.715820701646096 49 pop,GOP 0.6938775510204082\n",
      "1989 0.7916080316424349 30 pop,GOP 0.6\n",
      "1990 0.7957702002144589 36 pop,GOP 0.4722222222222222\n",
      "1993 0.6392117704306117 98 pop,GOP 0.5408163265306123\n",
      "2002 0.7400161307123375 105 pop,GOP 0.7142857142857143\n",
      "2013 0.6700323265268353 151 pop,GOP 0.7218543046357616\n",
      "2016 0.7459770812673094 182 pop,GOP 0.6043956043956044\n",
      "2034 0.7559484002916925 133 pop,GOP 0.6165413533834586\n",
      "2036 0.7629502225277611 60 pop,GOP 0.6\n",
      "2037 0.7438719658752542 50 pop,GOP 0.7\n",
      "2038 0.7684563619709784 45 pop,GOP 0.5777777777777777\n",
      "2042 0.7540457551990309 27 pop,GOP 0.8518518518518519\n",
      "2049 0.645207125340657 386 pop,GOP 0.7253886010362695\n",
      "2071 0.7890539367382928 82 pop,GOP 0.5853658536585366\n",
      "2078 0.798130324390667 2234 pop,GOP 0.5094001790510295\n",
      "2086 0.6161873764448318 24 pop,GOP 0.75\n",
      "2087 0.7710112926943292 41 pop,GOP 0.6097560975609756\n",
      "2099 0.7075349268592541 114 pop,GOP 0.543859649122807\n",
      "2105 0.7096162196486554 84 pop,GOP 0.6785714285714286\n",
      "2106 0.6195127458745145 809 pop,GOP 0.681087762669963\n",
      "2113 0.7562032597901904 40 pop,GOP 0.925\n",
      "2115 0.7436075207633028 29 pop,GOP 0.8275862068965517\n",
      "2153 0.63007788690351 105 pop,GOP 0.5428571428571428\n",
      "2157 0.7392747817251364 385 pop,GOP 0.6519480519480519\n",
      "2158 0.7859751439547685 102 pop,GOP 0.6764705882352942\n",
      "2160 0.7874121774541903 302 pop,GOP 0.6059602649006622\n",
      "2163 0.7576625352025124 144 pop,GOP 0.7152777777777778\n",
      "2180 0.7967329582301664 40 pop,GOP 0.8\n",
      "2192 0.6396504695299925 5378 pop,GOP 0.4460766084046114\n",
      "2197 0.6857773288888053 78 pop,GOP 0.6538461538461539\n",
      "2199 0.7267020770683922 8148 pop,GOP 0.5025773195876289\n",
      "2202 0.7560446591186644 75 pop,GOP 0.68\n",
      "2204 0.7735615707419692 65 pop,GOP 0.7076923076923077\n",
      "2207 0.7559241358649629 145 pop,GOP 0.6413793103448275\n",
      "2221 0.7546080604181501 49 pop,GOP 0.7551020408163265\n",
      "2223 0.7872676197928496 53 pop,GOP 0.7924528301886793\n",
      "2229 0.6788610485331283 155 pop,GOP 0.832258064516129\n",
      "2246 0.7803868917588958 31 pop,GOP 0.7741935483870968\n",
      "2251 0.6159358240168828 20 pop,GOP 0.7\n",
      "2255 0.6615534648558253 111 pop,GOP 0.7567567567567568\n",
      "2262 0.7591531319373578 45 pop,GOP 0.8666666666666667\n",
      "2270 0.7425112934664957 55 pop,GOP 0.7636363636363637\n",
      "2271 0.6377089682529358 45 pop,GOP 0.7111111111111111\n",
      "2281 0.7014465539025031 21 pop,GOP 0.7142857142857143\n",
      "2282 0.7844215541575137 38 pop,GOP 0.6052631578947368\n",
      "2286 0.7227062882727602 66 pop,GOP 0.7575757575757576\n",
      "2287 0.7458454457110227 181 pop,GOP 0.7900552486187845\n",
      "2292 0.7523052078734335 182 pop,GOP 0.7252747252747253\n",
      "2298 0.7517895357582784 279 pop,GOP 0.5698924731182796\n",
      "2299 0.7887449075285058 117 pop,GOP 0.7777777777777778\n",
      "2306 0.6864097109334627 158 pop,GOP 0.8417721518987342\n",
      "2307 0.7848871141457088 32 pop,GOP 0.625\n",
      "2308 0.642614578622359 3 pop,GOP 1.0\n",
      "2313 0.7461950278496535 46 pop,GOP 0.9565217391304348\n",
      "2314 0.7658502126934622 114 pop,GOP 0.8245614035087719\n",
      "2317 0.7475563718380313 18 pop,GOP 0.7777777777777778\n",
      "2320 0.780730097787404 524 pop,GOP 0.6507633587786259\n",
      "2328 0.7636819271280177 27 pop,GOP 0.7037037037037037\n",
      "2332 0.7342953976008809 40 pop,GOP 0.75\n",
      "2338 0.7707903488321743 111 pop,GOP 0.5135135135135135\n",
      "2345 0.6963516357001973 43 pop,GOP 0.5813953488372093\n",
      "2352 0.7866123123076831 51 pop,GOP 0.6862745098039216\n",
      "2355 0.7550172645970907 118 pop,GOP 0.8135593220338984\n",
      "2366 0.6609865005877058 73 pop,GOP 0.7534246575342466\n",
      "2374 0.693371722005373 186 pop,GOP 0.6505376344086021\n",
      "2387 0.7550234454023669 111 pop,GOP 0.6576576576576577\n",
      "2396 0.720426071583325 38 pop,GOP 0.6842105263157895\n",
      "2398 0.7383063758479151 379 pop,GOP 0.5699208443271768\n",
      "2411 0.6978047141040481 55 pop,GOP 0.6909090909090909\n",
      "2415 0.6630082144885184 55 pop,GOP 0.7818181818181819\n",
      "2417 0.7540360493059235 23 pop,GOP 0.4782608695652174\n",
      "2423 0.7385528453816409 44 pop,GOP 0.7954545454545454\n",
      "2425 0.6766397987858798 55 pop,GOP 0.7454545454545455\n",
      "2434 0.7780141059728605 65 pop,GOP 0.6615384615384615\n",
      "2435 0.6442434461550186 84 pop,GOP 0.7976190476190477\n",
      "2438 0.7759083615414903 40 pop,GOP 0.45\n",
      "2440 0.6611367555594113 333 pop,GOP 0.6816816816816816\n",
      "2442 0.6518616376436965 13 pop,GOP 0.8461538461538461\n",
      "2458 0.659418933296956 211 pop,GOP 0.7962085308056872\n",
      "2465 0.7774568911852363 317 pop,GOP 0.6656151419558359\n",
      "2472 0.7478081807851563 225 pop,GOP 0.6933333333333334\n",
      "2475 0.7350442584886288 40 pop,GOP 0.65\n",
      "2478 0.7746695523108005 61 pop,GOP 0.5901639344262295\n",
      "2480 0.7500402384703323 206 pop,GOP 0.5533980582524272\n",
      "2483 0.6702083704383066 8100 pop,GOP 0.49580246913580245\n",
      "2501 0.6959812398092973 156 pop,GOP 0.7884615384615384\n",
      "2508 0.7900446176614747 140 pop,GOP 0.5571428571428572\n",
      "2516 0.6706999209351568 83 pop,GOP 0.7108433734939759\n",
      "2522 0.7771641501699148 88 pop,GOP 0.7045454545454546\n",
      "2525 0.7643220126397939 18 pop,GOP 0.7777777777777778\n",
      "2526 0.7898337980917248 75 pop,GOP 0.6133333333333333\n",
      "2527 0.7803706818703627 143 pop,GOP 0.6503496503496503\n",
      "2529 0.7466585534442776 33 pop,GOP 0.696969696969697\n",
      "2530 0.6671432100623929 248 pop,GOP 0.6491935483870968\n",
      "2532 0.6251522782076664 52 pop,GOP 0.46153846153846156\n",
      "2541 0.6616350400460641 307 pop,GOP 0.6579804560260586\n",
      "2549 0.7332914970079363 56 pop,GOP 0.6785714285714286\n",
      "2561 0.7072801689884771 46 pop,GOP 0.6086956521739131\n",
      "2567 0.6926995466462379 114 pop,GOP 0.8859649122807017\n",
      "2571 0.6628855502471773 74 pop,GOP 0.6081081081081081\n",
      "2572 0.7271133881245357 37 pop,GOP 0.8378378378378378\n",
      "2582 0.6907476971253549 64 pop,GOP 0.703125\n",
      "2583 0.639847918117377 6 pop,GOP 1.0\n",
      "2585 0.746526179493072 62 pop,GOP 0.7258064516129032\n",
      "2586 0.6716115696555285 94 pop,GOP 0.5957446808510638\n",
      "2612 0.7629724233617174 53 pop,GOP 0.7735849056603774\n",
      "2614 0.679136066466684 7908 pop,GOP 0.49418310571573093\n",
      "2621 0.7177791045176225 271 pop,GOP 0.8044280442804428\n",
      "2623 0.7951264111028946 88 pop,GOP 0.8068181818181818\n",
      "2626 0.7221165402003016 36 pop,GOP 0.8333333333333334\n",
      "2636 0.7469534442729038 95 pop,GOP 0.5684210526315789\n",
      "2644 0.7558946834919194 79 pop,GOP 0.7848101265822784\n",
      "2654 0.21968187044103107 486 pop,GOP 0.5596707818930041\n",
      "2658 0.788208862196642 60 pop,GOP 0.8666666666666667\n",
      "2661 0.11062624403195621 7991 pop,GOP 0.49355524965586284\n",
      "2662 0.794141013870722 86 pop,GOP 0.8488372093023255\n",
      "2671 0.6306533328938625 725 pop,GOP 0.646896551724138\n",
      "2673 0.7658515294778903 86 pop,GOP 0.7441860465116279\n",
      "2675 0.779111999768527 114 pop,GOP 0.8333333333333334\n",
      "2678 0.772725939656545 32 pop,GOP 0.84375\n",
      "2681 0.795279749152332 158 pop,GOP 0.6835443037974683\n",
      "2684 0.7986988886046889 141 pop,GOP 0.7021276595744681\n",
      "2687 0.00013784961203212352 172 pop,GOP 0.7151162790697675\n",
      "2691 0.7360522851960899 63 pop,GOP 0.6825396825396826\n",
      "2693 0.6416474760567649 67 pop,GOP 0.6119402985074627\n",
      "2694 0.6507151841725413 1345 pop,GOP 0.6691449814126395\n",
      "2703 0.7675873605798946 71 pop,GOP 0.8450704225352113\n",
      "2706 0.7461502377349872 58 pop,GOP 0.7413793103448276\n",
      "2708 0.747352379143696 732 pop,GOP 0.6024590163934426\n",
      "2711 0.7929065123595391 470 pop,GOP 0.6617021276595745\n",
      "2712 0.6669283192394223 93 pop,GOP 0.8064516129032258\n",
      "2714 0.6362095333591449 11 pop,GOP 0.8181818181818182\n",
      "2715 0.6352900658114992 74 pop,GOP 0.7702702702702703\n",
      "2716 0.6830295106461275 805 pop,GOP 0.7478260869565218\n",
      "2717 0.7551564907189598 332 pop,GOP 0.6897590361445783\n",
      "2718 0.744997736625477 279 pop,GOP 0.5376344086021505\n",
      "2719 0.7446596920197639 1053 pop,GOP 0.5745489078822412\n",
      "2720 0.7441124232859905 748 pop,GOP 0.6350267379679144\n",
      "2721 0.7436226263454414 425 pop,GOP 0.6141176470588235\n",
      "2722 0.7432586807165057 749 pop,GOP 0.6234979973297731\n",
      "2723 0.7459599916098025 413 pop,GOP 0.612590799031477\n",
      "2724 0.7595847183608857 75 pop,GOP 0.5333333333333333\n",
      "2725 0.7662095616011861 32 pop,GOP 0.71875\n",
      "2726 0.7594290756541772 242 pop,GOP 0.6652892561983471\n",
      "2727 0.7588694939818326 222 pop,GOP 0.5765765765765766\n",
      "2728 0.6772017859329014 878 pop,GOP 0.5797266514806378\n",
      "2729 0.6834279098783231 489 pop,GOP 0.4785276073619632\n",
      "2730 0.6828207778033276 802 pop,GOP 0.5374064837905237\n",
      "2731 0.6932887729067534 1144 pop,GOP 0.6363636363636364\n",
      "2732 0.7308875668860166 491 pop,GOP 0.6293279022403259\n",
      "2733 0.7307162788711419 638 pop,GOP 0.5705329153605015\n",
      "2734 0.7330641648792862 502 pop,GOP 0.545816733067729\n",
      "2735 0.7433600688184895 514 pop,GOP 0.5622568093385214\n",
      "2736 0.741557363490093 423 pop,GOP 0.5555555555555556\n",
      "2737 0.7830945365899352 448 pop,GOP 0.6138392857142857\n",
      "2738 0.787388088012988 595 pop,GOP 0.043697478991596636\n",
      "2745 0.6137463109606891 87 pop,GOP 0.8160919540229885\n",
      "2746 0.5702975249612751 131 pop,GOP 0.7099236641221374\n",
      "2747 0.5659585084606815 38 pop,GOP 0.631578947368421\n",
      "2748 0.5634315294306249 285 pop,GOP 0.6175438596491228\n",
      "2749 0.6060912404435996 201 pop,GOP 0.8109452736318408\n",
      "2750 0.5833319940896177 260 pop,GOP 0.6807692307692308\n",
      "2751 0.5885125572605798 134 pop,GOP 0.7238805970149254\n",
      "2752 0.14735860516317834 220 pop,GOP 0.7318181818181818\n",
      "2753 0.59031269577704 73 pop,GOP 0.6986301369863014\n",
      "2754 0.5754960427965851 81 pop,GOP 0.8271604938271605\n",
      "2755 0.6009233749960311 60 pop,GOP 0.6\n",
      "2756 0.612249383724148 306 pop,GOP 0.6535947712418301\n",
      "2757 0.6844813628914258 81 pop,GOP 0.7777777777777778\n",
      "2758 0.6672346902813643 98 pop,GOP 0.7448979591836735\n",
      "2760 0.6652050943006291 70 pop,GOP 0.7285714285714285\n",
      "2761 0.6755141298725343 33 pop,GOP 0.42424242424242425\n",
      "2763 0.6649386359873328 4751 pop,GOP 0.4300147337402652\n",
      "2764 0.6602862260916719 4777 pop,GOP 0.4303956458028051\n",
      "2765 0.6476545139301159 5663 pop,GOP 0.4414621225498852\n",
      "2766 0.6475736822902871 5306 pop,GOP 0.4264983038070109\n",
      "2768 0.7559150038612139 396 pop,GOP 0.5454545454545454\n",
      "2769 0.756670992675294 158 pop,GOP 0.6012658227848101\n",
      "2796 0.754180940480913 2214 pop,GOP 0.46747967479674796\n",
      "2797 0.7766209525059364 1473 pop,GOP 0.41683638832315\n",
      "2798 0.7436913235417292 544 pop,GOP 0.34375\n",
      "2799 0.738233474396878 210 pop,GOP 0.29523809523809524\n",
      "2800 0.7373096752402197 1927 pop,GOP 0.3269330565646082\n",
      "2801 0.718841694667971 1450 pop,GOP 0.3137931034482759\n",
      "2802 0.7242028501459271 189 pop,GOP 0.30687830687830686\n",
      "2803 0.776365158684911 2440 pop,GOP 0.41270491803278686\n",
      "2804 0.7985939756073148 2002 pop,GOP 0.45404595404595405\n",
      "2805 0.7675142000962208 1921 pop,GOP 0.4367516918271733\n",
      "2806 0.7204668636486915 1174 pop,GOP 0.3798977853492334\n",
      "2807 0.7463222210108883 871 pop,GOP 0.399540757749713\n",
      "2808 0.742715176897906 866 pop,GOP 0.42725173210161665\n",
      "2809 0.7761665595853248 1 pop,GOP 0.0\n",
      "2810 0.7283299379043788 606 pop,GOP 0.7013201320132013\n",
      "2811 0.728477004236123 349 pop,GOP 0.6160458452722063\n",
      "2854 0.7381076093714584 360 pop,GOP 0.5194444444444445\n",
      "2855 0.7722751209628499 693 pop,GOP 0.5844155844155844\n",
      "3035 0.7788449504078991 649 pop,GOP 0.6409861325115562\n",
      "3036 0.781992125301597 566 pop,GOP 0.5918727915194346\n",
      "3043 0.7997192295162839 1858 pop,GOP 0.48277717976318624\n",
      "3166 0.146706265648611 1162 pop,GOP 0.4087779690189329\n",
      "4022 0.7968972693581277 5219 pop,GOP 0.4328415405250048\n",
      "4023 0.7973932089273386 1415 pop,GOP 0.5024734982332155\n",
      "4024 0.728811463296062 6592 pop,GOP 0.4399271844660194\n",
      "4026 0.7525517609971147 1437 pop,GOP 0.4850382741823243\n",
      "4029 0.0001496048268248025 66 pop,GOP 0.5606060606060606\n",
      "4055 0.748549532466091 7719 pop,GOP 0.4913848944163752\n",
      "4065 0.7308869346054492 824 pop,GOP 0.5691747572815534\n",
      "4095 0.7590233839107533 13 pop,GOP 0.8461538461538461\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 20\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.3 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.3\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1342906302894173 = MN std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15103798152027276 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  7.4659\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00324 0.46685 HD avg vs true 0.4636\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.04787, should have been 0.04983\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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trqrdwJ8Cv7xB4wRj7BfwUeDHk2xO8mLgR4CzKzxnB+Ps1RdYeKZJkpcD3wecW9Ep147jwM3Du/muA56pqouT3qnPoFZYVV1KcjvwIAvvJLqnqs4kuW24fjfwm8B3An8wPDO4VBvwtyuPuVcajLNfVXU2yV8Bp4GvAx+sqpFvHV7Pxvy39dvAHyV5nIWXsN5VVRvyf8OR5E+ANwBbk5wHfgt4EfzfXp1g4Z18c8CzLDz7nPxxh7cISpLUii/xSZJaMlCSpJYMlCSpJQMlSWrJQEmSWjJQkqSWDJQkqaX/BacyS9eVboe7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the GA map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  713307.375\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HZL4oB4jp5/amX4PbO4UXjDHz01xzEfBr4FxjTMZsjdraWlNfXz94QirKCcBgRAX9yb3vBK6AgwhbYFlWUvatP//gOqf8hL8AXkjghrMrmTp+JJ1HernfyU9wKQwJd101nzud3s3e+4WElVfN9/IVfr3mU9oOJJt7XBLLbmRi9sTRbNlzOPsbcowAT/3FlwfV6SwiDcaY2kzXDelOQUS+BDwAXJ6NQlAUJZn+tIZ0lUfJqMK4MMrV69tSlpsIonRMEbsPddsdzCIxVv62kUljR7CmqZ2o4wuYP2UcK19ojGu6EzPw+Putdi/lgIm7J2p4aeNOyscWxYWSRpzjl8+fzMoXGr0CfKnoj0IIWTB/6rhhoxRCAj+6ZsGQRSEN2U5BRCqB14FvJvgX0qI7BUWJ5541W/jly03EjL1Crpo4htGFIc6pmsCh7gh7fbV33mhq90Il3YSwaNSkDfsMwt8PINUMUj62iHZfNM6xYglYjuloMGct13EeiZl+l/IeCPPKi1k4vQQDjC0Kx9WMcin0ZVAPFkO+UxCRx4ALgVIRaQP+ASgAMMbcC6wAJgC/FttpFclGYEVR4vG3wYwZ2NL+BUDGZjVuQlgqwiHBGBPXO9lfVtoSqDxlFFsDmuUAg9IEJ1FeMLYi66diEOc/QWtgETxTVbZjlo8tGvDn+2zPF/x4ib0TcKOMEumN9rXp7DzSS+POQ1mV1RgMchl9dFOG88uAZbl6f0U5WaiZXsKKK6v54bMb+hsBmZZY1GCF+tbOgm3asJz6PKGQxZyyYrYfOOJ1DxsI008ZRcv+YMWSSGHYYsWV1WzccZDH32/NehdiWUJN5XgaWjqI+TKlLYEvTR2XsZJqIourJvDsBzv6dY9Lb9Rw96ubqZ48lifWbQu8RrALArr9HCB1WY3BRqukKsoJQLY9gv2MKEj/9Y9h2/JdDHZPggtPncjF88qIRmO8+vFuBJg9aUxW71kYTn7Pzu4IYasvfFWwHdkVCclkFeNHeL0Tpo4fydyy4pTvMf2UUXHHojFjO9HFVgTeZzKZd1RBPP/hwBQC9NV0undtc2AUlQC1M0riFILLSxt3Dvh9s2W4hKQqinIM+E1I2a7ZuzM4a4MwwOuf7MbQV9cnEjXMLB3tma3SMXnsiKRdwf7DPYQsuGlRJfOnjPMc4E27OuPMK7sOHeWx91pT+jIs7DGqp4zjh88Gm2USC5b2d3/jvu9g+UmCCIWE9a0dgbJdPn9y7t7YQZWCctwxGOGXJxpu3aDVTrJZNpNWf+a10YUhunqi3m4hzqIv9mSZTRhoNJYi78E57DeN1DXvi5v8/SkTQW9TO6OEnyxZwD1rtgyqGQ3szza3rJiOrp5B95UkEvXtztxdw4iC0PHvU1CUXNCf8MsTnUTlWDO9hKfXt+VkFXu4J+r1Igg5OQpuYlvM2FFNIUvSOq4B2g4cTRnhk5i0llg4L/HaxDE27eqkoaXDvi8kXlZ0EILd7tMAr23anfGZxQyB3dlyhRfdJVBTWcIdV8zL23urUlCOK/zlEbp7YzydprbOiUyicnQbwe8JaP3okjiRSoponFR4E6cI3/7yDO57q9m7PxI1LKgY59UZSkfQ+ZAlnr8A+prwVJaMjMsfsASWn1dF8cgCnqzfFhf51Hk0wk2r3uWKBZPpTTPLC3YBPLCVWS5NQQPBEF8y/N61zVROGJ23kheqFJQhJ5M5yH9+cdUEwiHLa8v4ZP02rl1YcdIphtXr2zz/QXdvzLOhhy0h7JR79iPYTegxpq+ks7Pqz7IxmEc0ani3eV+cQhGBG86qZNOuRq9XQn/m2rllfY7qxCY8iRSPLOD2i2bz4bYDSeGwPVGTMSpoSslIdh08yssf7+6HhEPLSxt3qlJQTg7cCpxuwTI33FDAWzkmmosuPHWi94WOxrKrQX+sfojh5MdoaOngqYa2uLLN3oo9Zrjx7EoEu7pndyTGOVUTKB5Z4GUvr/xtIx86K/r+KgSwHaGHuyNxx04tK2ZueTGYvsQ4IdnHkMp09PHOTm5+oM7rp9CbwmRkiVAyqpCGlo6kJjhB41sCl8wri1MA2zuOZPlJhw/5cDC7qFJQhgy34bpb/bK7NxZXsOzJhjaur6mIq6a5en0bbzS19w3imyTc8g1+peL21F3h1NIpKrD41jkzvGSgueXFGSf7VKaaoVIQ6fr+WtJXXsKT9+vVcWWpN+08dEzvH42apJIQm3Z1ct+bn3n5CqlMMqeVF6e0zbvVUlP5EkRspXfnsxuwrPjdkCVw8bwyZpWOjssQXn5eFZdWl/NGU3taH0MQbpLcUGMJtsLNE6oUlEEn21V1XfO+uC+dSHyp4d5IDMGOO++NxAhZQuP2g3GJUtGY4QfPbAiMfHmyoY27vl7ND5/Z4JVxONobi+uxG7LsrN1wyOL6mgquCzBF+f0YPb2xuJ3NUDi6/RVG/Z9ZgGXnzqSjq8czLfX0xpelrmve168S1EGkunv3oaMZS1wvnF7C3PLiJBOPACJC55Fe6pr3cdfXq3mjqZ3XNu12dh5940ZNX4SOBXxlTimXz59MR1cPnUd6vb+FsAWXVpdTM72Eb9RO45F+NK4RAQn4pGdW9D/R7VgxhrR9KQYbVQrKoOBfqcetUtOsqktGFcZ97c6dXcpaX+N2EaieMo5rF1awen0bTzW0sWF7sCMzaB7qicR48O3mtHV9XKXUE4nx2HutPL2+jUeWLQbsL6Jgy+AqJhEhZozn6M7nl9XPdQsr2Lj9YJxj1wD/9ofP+c5XZvY5KrGfs4unUJw2l5lq/ZSPLeLUsuK430sqbjirkvc/35fSph+yYE9nN69tirflnzWjhP9qPUA0Zrh3bbPX2vKRZYu59YJZ3t+Vf1eJI3thgeUVyUvM0YjG8BTitQsreLJ+W9a7BWMgEnBpvhUC2L+fpxraAhcsuUCVgnLM+M0rlm/SPNob487nNhKL2fVqVl4933OWNbR08MS6vpVbUIG1mIEfPrvBiw93Mzz7U7SsNcvyCThjHu2N8fOXNvHBtgPeBFIQEv6nr2zziuc3EnOctb+p35a3LyvEP+twyMJK3F05TmB3tWxJfLZzYh/kjTsO8phTtTSIv7r4VOaWF/OHz/YSjTlF6ZwieqGQMGfiGDq6erjmzKm07juc1slrDEnO3bAlnFpWTENLR5xyc01Jt180O+7Zuru0sCV8o3Ya1y6s8HZyiR/BAJ1HerlnzZakBcjxRjQay8p3NhioUlCOGb95xRgT15TLXYlHYoYVz9ntutc4ZgH/6t4AjQG7gKD48P58ubNZGU4cU8i+wz2ePIk9BXqjhjea2ln1zVq7do5vzEjU8POXNnHB3El58TH4n3U0GuPieWVJDegnjR1B4e5Or9FNYttMN6ehoaWDxh0HOa0s2M4v2FEva5racXPOjIGvzp1EaXERv6nf5t1339rmjL+XxN2cJba569LqcrvbmvO5XFOSf4cDdmJbKh9QqmzuVW8122P2M/x2uFAQEmIxE/h7zBU5LZ2dC7R09vDD7anrTsDpMlvzUZo4Fwh2CQWBlLbpcMJuyCWoh8FAlYe7U3An/EeWLaZpVyd3PruBmLEnkceWnwMQ11YzcSJNDPtMl1CWeNwSmDxuRFy/g/7i1h9yfTlueYvOI7084DTeSdxdpuPR91r5ofMMThQsgR9fs2DQghqGvHS2cuLjn+zmTR7rFRZLt844Xr+zBnviSVfOwd0NJTahd8097irYtZdD+onbT0NLB6vXt7G3s5vz5kxkUnGRF11V4zhvE+93dwNBGeCJYZ9xSW1AyegCOg73pvTfZKMQCpx8iXQ+INeX4z6TuuZ9xJyw1qDnmfhM3M/cuONgXhWCBVx8ellW2dADJWZs09/tF83OzRukQJWCkjX+L2HTrk4vzDPxO3G8btWzwZ8TEEQ0Zrjvzc84Y9p4FldN4Glfkpl7f2/EzsR2TSZhS0DEa2254spqGnccpL2zm0nFRVRPGcddz2+MM4WFfCY6VzG4SuCeNVs85eA3N/nfd29nd8rfkwH2H+4Fjm1nl205bb8PYXHVBM8vBfbzfDqgF7S7O+11+kmXjRs5QCn7T0jsRMCL5k5iYnER/9XSkbMSGC98uCPvoc9qPlKyIt6Z7BZFU9IRshOI41aSFnbEzLULK+IqfkJf45rExvTp4uVHFMT3R07cFQCeuSkUsojFYkQGkLCWiRFhi6NZ1ihKxH0m7uf42Yub4nINwk7Wtd+cdMvD9bwygIzkU0YVUDZ2xDFN4peeXsZFcycFRjzlAv/v+FhQ85FyTPzsxU38rnEXl1WXc8cV86hr3ud9AU4ku20uCcovu+T0Mi6cO4k3mtrjdg8hp9qcSHKcfyxNm0g3DwHg7lc3x+0K3Ogd1yyz/cARHutHrH5/CFIIYIebNjjhpqkYVRTiknll1DXvo2lXJ407DwVWR3UT1wBe/6Q9cKxM7O/qZX9X74DudVnzSTuTiovyohDANrHlK/IIVCkoAfhXaveubWZ9awcLK0t0ZzAIvPzx7qRoIbDNJLMnjWHKuBFJOQHhkHDmtPHUb02usS9ih13+yb3veKGpbp2jxVUT4pT7dQsreKofsfqDgQFuPGua14/4fseJ7OeL7mhcKKurENwe0nHJigaeWNdKbAhXJp7JNE9REyKSt8gjyKH5SEQeBK4E2o0x8wPOC/Ar4AqgC/iWMWZ9pnHVfJR7Fv7oZc+mrAwtsyeNoXXfYXqjBkugZEwhezv78g7ODliJu2Wh2w8djUu2mjimkJmloxk3qpCDXT1Jobe5wM0Y95uznl7fxnvN+5JKZfixgAUV49i081CSEvva6WWs/XQPRwfQJGgwsLB3fOtbO9j7Rf873vWXs2eU8JvbvnzM4wwH89FDwL8AD6c4fzkwx/m3CPhX5//KENLQ0qEKYThhjDcpRg3s7ezx+hoAbNlzOGnlbUhOEgPY80UPe77oQcjcinOwiDmr6p5IjJW/baRs7AhKi4v49rlV3PnchkATm2AnyBWFrSRndUFIKC0u8pzxj73XmjZjfTAosCSuFHeM4OebK2Y7bUfzVZQxZ0rBGLNWRGakueRq4GFjb1XqRGS8iEw2xuS+CakSSENLByt/2zjUYig+glbTfh2w/3D/V6oGOJKnVbbrJI8ZtxeyvXMJW3Y9oyh2suN5s0sxwITRhbzw0U4ibk9lB0vsc3u+6OHR91opDNv5DbUzSnK+40nXmyEfXLewIq/NpYbSpzAV2OZ73eYcS1IKIrIcWA5QWZmfmuInG+4f3UD69g4nLIGrzpiSsaa+kntGFFhcVl3OcwG/C9t57Ey2Bt75zM5PsCQ40ipm7J2Oi5vfUBCSpLyRsCVMHT8yqRf0UFM+toieSKzfjm63BldiEEGulEJ+9pDBSMCxQJVsjFlljKk1xtROnDgxx2KdnKSqH3O8ETPw+d7DcX9cl55exnlzSuOuKx0TX0LhjAq76J3LmKJQLsU8KTh7xik898GOrDqxRZzdRCQg7yXdfT1Rw9yyYsKWINgKYdm5M9kZ0GthMBFgXj/LWe861D2gyKdH32vlifpthEOWlyORS8fzUO4U2oBpvtcVgC7vhojEOjPDnVBCITg/ZWNHUFTQV/vntgtm8UrjLt7yRfXs/aKHsAWxmG2/PqdqAvOnjmNPZzevf7KbL7qjefokJy7ZVFbNRDYBPpt2dXL+nFIOHumlbOwImvceDmzSM5hccnoZk4qL8ta3ORI1fO30SV5S5HHpU8iC54Hvisjj2A7mg+pPGBoaWjq467eNec0/GD8yzIEjkcwXpiBdVOWFcyd5JZfd7OtVbzUnXTejdAxb9x72SjZD+rpNSv4x2OaM2hkltHd2J7XfdOlTQLkpbV06pjAu0mhW6WgeeDv5b2pMUYiu7mhOnN+lxUV5KXmRM/ORiDwGvAvMFZE2EfmOiNwmIrc5l7wINANbgPuB/zNXsijpcU1H+WT0iIKcjd3R1UPN9BLvC3RnikJpVaWjk8p0DIZCKAgFWUaVgRLDrly748ARrjlzypDIsM+nECygceehpMipkCVcdeZUfrxkAWdUjAu0jw+UcEi4zmlPm2tyGX10U4bzBrg9V++vZEdDSwcfbDuQ9/fdeSB3fXL97TnfaGoP3FWEQ0JV6ei0pglx/vVXXWZb80fpI5sdWk/UsK4l97kVQfhFC4WE6sljeWfLXu9vy3I6tT3+fqtXv6pp9+CVwfiT2mma0azknqGMOMqliebZ/2pjfeuBtG0hTczQmKFXscH2N2QrbPnYInYf6j7unfX5RoCbzq7k092drHOytl2T0f6uXra0f+Fdu70jfjFRPCJMVeloZpaOzsqpPRhMP2UUD727lZixfVs100vojsS8Lni9kRgbdxzk/DkTeTUge30gzJ8ybhBGyY6hjD5ShpjVCRU8hxtTx4/gtvOrvNr72f6xvr+1I1AhWGLHx9tNXKC7N+qNnYpsG7db2F3KLP1G9RsD/GHLXk8hgL0723+4h0tOm5T23q6eKBu2H8ybQgA4ZXRhXB2w+pYOTyFY2GakJ+u38fLHuwfFtyDEd8/LNbpTOElpaOngqYa2vFR4HGg5gtOnjOOOK+ZxaXW55zS+783PBpRNagksP6+KB9/ZSsSpFOomPQlO565j8KvMLS+mdd/hwAxdJTNBDuQtew7z2Z5kZ66fbJX2YHH2jBI+SOiL7WaXC/CVOaWMLAgNasZzyCKvtY90XXOSkthkJVcEKYTExXnQH2HYgtsumAXgOY1rppdw6wWzGFFgZVzhJ3Lu7FIadx4K/MwGjkkhAGw/cCSu3LMyOLiTr2CvwM+aUUJh2I7XLwyJXV02T4QExo8qTPm9CVnC9y45ldLioqRzMkAxBbjhrMq89lPQncJJyuKqCYSs5DLN+SAUEr46dxIHunrojsQ4p2oCD/7hc7thSkj4k9ppXOd0FQM7eeeljTupnjyW4pEFrLiymo6uHj7d3Zl15vJgxMy7CFB5yqi4jNlDRwceXttfLOydSb5i5PtLeXERuzq7B3x/UG6CiF1HacP2g9z19WqvReUrjbu8Psw5R4TXP+nzEVjS5yC3xO714P7N/mZda3zfigHIZ4m9g702T1FHLtpk5yTm0fda+cEzGwZ1TAsoG1vEzkOpJ4WQwI1nV3qdx9xojaBetEEyuk1H7n51c1xCWq4pCAkXzZ3E65/sjvvC57KCcuLYAvxkyQLmlhfzjXvfGXY5Ff3N8wg7LTvB/mxLF1V6rU9Tjb9g6jgmjR2BAK83tXv3JzL9lFG0dnQNWhdAfzMkF7cGk38RA311xD70VanNlpAl3HLuTIpHFgxqolq2VVLVfHQSs3RRJT9dsmBQx1x+fhX7UhRps6SvlaFbOdOt5eL2ok38Ary0MTmf0a39cvn8yYMqeyYunGtnlCZaDyynxIKfCsdJ7quc4YQtJnNGxTiuOXMKMyaM4pozpzCiwDaPhC1JMjtYYjsd65r3DcuWp6kUggClxclZ836F4OZ3zJ8yLu65JY7/YdtBXvl4Ny9/vDulQgBo2X/sCsFfysJg73L9v5JoNMbU8SOT/m5rppdwzgD9ACGBS6vLA78P+UDNRyc5SxfZBQbvdPotDxQL+PGSBXR09QTG6Ycs4UdXz/d2A2DX1XdLUaRypF0+f3LcbsDfQKZmeglvNLUHOvUmOhmo2XyisGXbbaunjKOjq4eSUYWsaWp3yoj3KbhJxUW22S2hxMZXT5vEpOIir4xzSOBXNy2kZnoJnd0RHvW13bzk9DJe9UWlFISEFV+vTlpl1jXvo2RUIStfaPSUp0V83ZuCsJX3pMNUpNotuYqwIGyxcFpJSgdsyBJ6ooZH3mslHJJh47DfvDveRFfm+At2HjwKJn0dokwhz6mIRA13v7qZ711yqioFZWhYuqiSueXFSZE9QV/04qIQnQl1geaVF/PjJQu8PsEFIfF6AFgCl8wr49YLZiX9gbttItNtkV2l5fcp+K+/9YJZgQ1Xzqws4c3Ne5ImTbeUs79pvTEwZfxI773c921o6eCm++s8xXWtYyL40TULvCzpgpBwm1NSw//A3CqW1y6sYLVP+d16wSxuvWAW9735GbsPHQ10ItZML/GOzS0v9hREonntsVsW8/R6O4Js/pRx/DBF5nYmwhasvHqBpxAbdxzkjaZ2th/Ivqhcqre1xFa4rl381U27A2X0+7ZSrf7z1OgsjkRZ/c8kZAkrrqxO+bebuKDJBjdZ0g7R3Z/TEtmpUKWgAPZEtOqbtZ5T9/L5k5lbXszT69vY09lNaXER1y2soGlXZ5yN//w5pTz8nUVx4zy2/Byv3O+1CbbWxPfM5g9+6aLKuAk7cYxHli1m9fo2flO/jWjUUBASJhUXEXGWmwJMnzCK5efPiptkV77QmHanUjO9hMduSVZcrhJNPF4YtpLGc+XzX9vQ0sHaT22F1bS7kbnlxQN6Rv5zDS0dhC0ZUKvN6injkmRoaOnghvveSTKV9ZdIDD7d3cndr25mwujCY/KBZLo1F3WrXHNR0O43FjOB+QP+ZjjXnJl9GXdL7N3orkPdxExf/233byYfDXZAHc3KAPArjlST9VDg/+IA3PxA3yo/aMU12F+0bMe7Z80Wfvlyk5cR+9dfm3vMhc78Y6ZidGGIylNGsXl3Z5z5y41ySXxGj77XesxmxXxhCVw8r4wDXT2sy9B0J2wRp+zOrBjHxh2HkiLxXD/Ht78yk/vfak4ql1IYtnjslsVJyvSmVe967VNjkLVfI+RkVfqf90+doILBaLAzHNpxKico6VbuQ0niqjqTeSrbncpA3z8Vi6smBO4qjgX/mCFL+EbtNPZ0dseZAw/3RAPDWFM1buno6mGoFo2W2AULL5lXxoPvbE3pO5lXXkzxiDANLR28tmk34ZCVNOknknhu16GjfHnWhKSwZYOdHNe481BcvsSsiaNZVDXB2wX7F0kbdxyMa5+aSEFIUtbGivoz4ejLZHYLVuajwQ5koRREZBTwN0ClMeYWEZkDzDXGvJAzqRRlEBjsSX+wCDIp5WJM10yVKaPcdQSXjCrknjVbvPtdReNOSOkYUxjii57B60HhNku6tLqcS6vLueXf1yU1qBHgsz1feA16wJ40ly6q5L3P98fVTEpH+6FudqcIoQ5ZwuXzJ7Nu635Pif/8+jO857v84XpP8b716V6mloxM+179KZYYColX+t1ynGC5brADWZiPROQJoAH4pjFmvoiMBN41xpyZU8lSoOYjRcmehpaOjKVBZjj+lrue30iv45N5bPk5cbbsziO9NO48RPXksXR2R3j8/dYkE5RlCdFo9p3TssENm06VUxPkfJ5XXpxkIkukyFfWJJ0D++ZFlfxkyYIk06BbTHKgJVyy4dLTy7jtglme6chNkBvoLn0wzUezjDE3iMhNAMaYIyIDTdpWFCWf+AMIHvzD54Gr5+Xnz4oze/REDavXt8XttNyJyY2IAXjEl2AWM3CJk8fxnx/t4OOdg5NtvdfJjF66qJLWfYfjSom44a6Jk382md6uQnDTIYKUQsgSqp3qpIm7znQ9SMT5z0Asb34F9ebmPUwsLvJ2akKwY3uwySZ5rcfZHRgAEZkFDDyHXVGUvLN0USVL/tvUuMSr8rFF/HTJApYuqkxKqvO/DrJpX7uwIimx7oATMjuQCKhU+OsINbTGO5CrSkdzy3lVAx5bgMoJo6idEWy+i8UMK19opCGgh4NrWguJnZU9e9IYwiEhJLYyGeh2afqEUd6zj0ZjXrHGfPRmdslmp/APwO+AaSLyCPAV4Fu5FEpRlMFncdUEigr6HNz33FzjrX6vXVjBkw1tcTkZ/vsSHeM100uYVTqaLXsOe9dtP3CEP7nv3UGLVhL6+gg0tHRQnzA5f9EdSVIU/cEArfu72J6i4ZPbGyHIsVszvYRvnTOD3zXu4sxp45lTVuzlkpSMKuSu3zYOKLEwbEnc7+jahRVcu7Aib+GokGVIqohMABZj/57qjDH5KziTgPoUFGXgpAub7e+5RDt/LvIEBLj1/CqKRxbwj79vCjw/kLcsH1tEe2e3Y5YJHkOAooK+EFD/M0jM10m89gfPbOAxJ5O9P5w+uZgfXbMgJ0ogW59CNo7mrwAfGGMOi8ifAguBXxljWrIQ4jLgV0AIeMAY87OE8+OA/wAqsXctvzDG/O90Y6pSUJThgxuOObIglDJbuT8UhoIT8M6fUzqolW6vOXMKL27cRW8kRthJUPNHMSVm4ruOZTdXYG5ZcVKxO7fQI0B7Zzdvbt4TV2Y7m0cze+JoXv2bC4HBz6MZzIJ4/wp0icgZwN8BLcDDWQgQAu4BLgdOB24SkdMTLrsd+NgYcwZwIfBLEUmumqUoyrBk6aJK/r/vLOLCuZOwxJ5cLbG75hWExC6CaElg/wshuUDgFQuCixx+tP1g4PGB8uwHO4jG+qKP7rpqPn/ztblOEUNbqrWf7vGu9/tVeiIxysaOSBrTsoQn6rfxyHutvPLxbiLRGJYldiMe530KQ+ljdKomjgH6WuX+8uUmbn6gLtCvkSuyUQoRY28nrgb+2RjzK6A4wz0AZwNbjDHNxpge4HFnDD8GKHaimcYA+4H8FaZXFOWYaWjpYOULjURjxgtN3XnwqNd7+UdXz/cmWj9fqhjHU3/xZX66ZAHnzSnlp0sWcPeN/42fLlmQFO9/4akTk+53FdBAicbsCSgSNV6V3uKRBcSMiXOqA5SM6ivRETN2xdyfLlnAGRXjCDlRULGYiavbFDPxneFEbOUzsiD1tHvhXLv9aJBzP19k42juFJG/B/4UON/ZARRkcd9UYJvvdRuwKOGafwGeB3ZgK5objDFJ3hkRWQ4sB6isHH6ZtIpyMuNOYAbHp+DkKkRjhinjR9LR1RPYzKls7AjPPJJYjHDposqkcirlY0dw31vNXqhnQUi4cO6kY259GQM6j/Ryz5otlIwqDMw27+jq8XwPlvP69otm09HVw4btB/s+ewKJhRdf2rgzbbe4NU3tLF1UmZOs92zJRincACwFvmOM2SUilcA/ZnFf0CdPfGx/BHwAfBWYBbwiIm8ZY+JqzhpjVgGrwPYpZPHeiqLkicQSG4gQjcZPZoVhi57eGDHsiSEcEt5oaufVTbtT1vNJLKdSPLLAm5gF+EbtNK5dWMHrn7QfcwfB+53ubSFLWOZrcAN4ysIfFeSe8392kfhOhhZ2X/AH3v6caMxWlJmqpjZs3U9DS0dOst6zJaNSMMbsAv4f3+tWsvApYO8MpvleV2DvCPz8OfAzxzy1RUQ+B04D3s9ifEVRhgH+SrUCXl8K/2TmTnBu2Ob2A0d4/P3WjPV8/M7WOOUTsrwV5rJzZ3Lf2uakFWc4BJEsK2+4Vp9IzPDA25/zxK3nAMQ5l4O6A7qf/d43P+PjnYfY0XHEkyMUEq9MR7Zd2PZ39XLzA3WekhxW/RREpJP4lb0B9gJrgO8bYzIZudYBc0RkJrAduBF7x+GnFbgYeEtEyoC5gHY/V5Q8MxiRLk/Vb0sqk+GSOME1tHRkbLKUGPHzyLLFPLLM7iHxZP02Hn+/lafqt3mOXD+FYYtRBSEOHOlNGjcTkZjh6fVtTBk/MrA7YCJNuzp5JcCEFYsZ6pr3cftFs5k/dVzWrTnzUfQuHSk9HsaYYmPMWN+/cUAt0Ajcm2lgY0wE+C7we2AT8BtjTKOI3CYitzmX/Qj4sohsAF7DVjZDlgOhKCcjgxHpsnp9Gz2OL8Etk5EOd4X911+bm7IUdKrqoFPGj/TCR3ujJrjInDGcWjam35/D5Yl1rZ5/IVM2cVDLWLfIoHvPtQsrKEzVY9SHlcfM5VT0q3S2MaYD+CcR+bMsr38ReDHh2L2+n3cAX+uPDIqiDC6DUZo5XZmMVASZR/ytSLcfOGKHdEYNIUvi7PhhS7yeBUE6oT9Z1eGQYBIihaIx25mcjV0/scNaUI7D6vVtXHjqRN7espeuNNVkF0wdl9SeNd/0u5+CiBQM5D5FUYYngxHpkq5MRrb4zUVJc3pioSVxXM7+8B7/aUuSymK4zJ40hs/av/Ac1tWTxzKzdHRchzTLwit/kakBkusMv2fNp2w/cJSYgdc27fbCS/vTwe5AV++Ql3tP51O4NuBwCXY00lM5k0hRlLwyGJEuqVqX9gf/jiWRaLRvB1PXvI9I1A6BNU5uROI9MyaMTtlP4ZLTJtHW0eW910dtB2nccSj+IgOPv9/K0+vbkhzMQf6XueXF7DrY1785auCHz27g4nll/Wpp2rK/i0ffax3SJlbpVvxfT3htgH3YJS7+M3ciKYqSbwYj0iXdGK4JRUjdtzuxqY+XF5BgZ0/c2VxWXZ7UB7kgRS6AYIe2PrJsMXe/upm3P93r5BgYwpZgjEFEvAS2nkiMFc9tJGaMF4G08oXGpNaYq9e3JZmx3EZB/eWJdcNUKRhj/hzs2kfGmD/4zwUdUxRFCcLtW+zWNHqyoS2ptzHE71j8FUdThYG6q/W65n1JRe027+4MLHRnsLOTa6aX8L1LTmXd1v1eAxs3P6FkVCErX2j0cg/cHIPeSIyXNu4MzDR+qiHYsV5gSb+L9n3YdpCfvbiJO66Y14+7Bo9sfAP/L3YRvEzHFEVRkqhr3hcXIRTkzPabZDLZ8CF5V1JUYNHdG/MmX+MkorkrfhcBr1FNzfQSVlxZzYrnNhKNGR56d6u38p9bXuwpp5UvNNLTayuI6sljPUUiIpSMKvTMWUEUhi1uPb8qrjlQNty7tpnKCaOHZMeQzqdwDvBlYKKI/LXv1FjsqqeKoigZWVw1gQJf9dNEZ3ZQPkJ/TFnuzsHNX4jG7F7Gri+g80gvD7z9ObGYobAg/r07unqImb6dgKusEpWOa0J66N2tfOucGV6W8soXGllxZTWF4Xil5HLDWZXMLS/m3/7web/6M4Md6jqslAJQiF2kLkx8AbxDwPW5FEpRlBOHmukl3HXVfJ5Y10rZ2BFeqKbLYITEupN4qoY0l1aXe8fBLl2RmCWdKvLKVRyufI07D3kmpZ7emBe66iql3qhBxC5xsXRRJfes2RIYIivYRQEnjR2BANv2d8W1Er18fnDF2FyTzqfwJvCmiDyUTe8ERVGUINwqqj2RGE27O7n1gllx5wez+FtQ5rRrBtpx4Aj3vvkZb27eQyQanyWdLmoqUb7qyWO9vIQYfT4KVym5DvVLq8u9+y3Hee3HsoSysSMwwMTiIm69YBZNuzrjigAOBdn4FB4QkW8YYw4AiEgJ8Lgx5o9yKpmiKCcEmXYCgxESGxQm2tDSwU3316Vsi+nKkmjK8h9zf050bLthsJbYOwn3vs4jvfxmnW3CWr2+jUeWLQbgotMm8ZqvCZFgRzz5K7y6DvihjDyC7JRCqasQwM5qFpFJuRNJUZQTiVyXgXajmxLrLj29vi2lQnDLUJSMKvT8GWGnIU4kagg5zXCivjH9DnD/5/GP4bcS9fTGWL2+zZMjbNmlvg3w+iftSSal3oh9/VBURvWTjVKIiUilUx0VEZnOwNqiKopyEpJpJ3Csjma37hL01V2qmV6ScpIKW/DV08ooLS7ijaZ2z0Hc69RuAuKa5fjHDPo8q9e3BTqZLScc1VUW0ZjhjGnjAXg1oICeiB3a6jdtDasqqT7+b+BtEXnTeX0+TsMbRVGUbEiX2Hasjua9nd1xr9//3O5JcN3CCq9ya8inCMYWhbnfiR7yk6Jihn0uxedpaOngSadSq//akCWsvHo+c8uLWZ1QDfaVxl1JCiRkCRefNsnrcz2UlVKz6afwOxFZCCzG/rz/QyuZKoqSLZnKch+reanJF7EDsKX9C266v47HblnMY8vPSUqGu/PZDUnZxwJcPK+MN5vaPSUiIkSiJm0tp6fXt8WFmp49o4Q5ZcUY7NIXibsKsBv6+Dmjwi6CB3Zf6KHotuYn28J2UaAdGAGcLiIYY9bmTixFUU4EsjENHYuj+dH3WmnZ35V0vCcS4+n1bVy7sIIdB47wq1c3E4kZLJHAqqpFBRa3XTCL2y6YFehoTiVT4lDjRxXa5izn/ROb5dyzZkvS+1dPHZfUjGhY+xREZBnw37E7p32AvWN4F7uFpqIoSkqyNQ0NtPZSUC8Dl093d3LzA3UJ9n5DyFduW5wy17c5YbKJE3ImmfwmqoKQUFpclPbzLq6aQKEvkS8cEq7z7UKGqtuan2x2Cv8dOAuoM8ZcJCKnAf8zt2IpinIikIvII785KrGXgZ/uSIyeSJ9CEPCK2q1pauf1T9oxxvDWp3u4aO4kL5fCEtsfkE1oqJuY5+YWzC0v5imnhHgolPx5a6aX8NjyczIWBxxKslEKR40xR0UEESkyxnwiInNzLpmiKMc9g92APsgcVTF+BG0HjsZdVxgSbjirkqbdjd4EfX1NBdc5k3BHV4+XN+AWunN3FDFjWPHcRs8nkEkeV5ms27qfFVdW93mrU3ith8NuIB3ZKIU2ERkPPAu8IiIdwI60dyiKojgM5iQYZI7q6o3vZFYQEuZNHgukttGny1IGO3w0m+ifRHle2riTiFMCI9sxhhsZm4YaY5YYYw4YY+4C7gT+Dbgmm8FF5DIRaRKRLSJyR4prLhSRD0Sk0Rf2qiiKkoQ7mfv7Jo8sjF/b9kYNH7Yd5AfPbKBpVye3XzQ7pXPb7RFdPLIgLuzU3/6zP/JcPn9yVn2dhzNpdwoiYgEfGWPmg1cPKStEJATcA1wKtAHrROR5Y8zHvmvGA78GLjPGtGqmtKIo6QgyR91+4Wx+8MyGwOvTVRoNKr/t9ylks8IPksctuz2UEUTHQlqlYIyJiciH/ozmfnA2sMUY0wwgIo8DVwMf+65ZCjztjm2Mae/neyiKcpKROJm7k/5LG3cyYXRhXBe2aseMlM2YA/V9JMoz3H0GmcjGpzAZaBSR9wGvt5wx5qoM900FtvletwGLEq45FSgQkTewy3P/yhjzcOJAIrIcJ4u6snJoi0UpijK8aGjpoKOrh+9dcio100soHzuCVW81Yww89O5WLq0uz3rVfzxP5oNFNkphDHCl77UAP8/ivqAmqYnu+DBQA1wMjATeFZE6Y8zmuJuMWQWsAqitrdW6S4qiAMHRSMUjCwB7sunutZPIdLLPnmyUQjjRlyAiI7O4rw2Y5ntdQXLUUhuw1xhzGDgsImuBM4DNKIqiZCAoGmlx1QTCIcvLUXiyftuwzAcYrqSMPhKRvxCRDcBcEfnI9+9z4KMsxl4HzBGRmSJSCNwIPJ9wzXPAeSISFpFR2OalTQP7KIqinGwERSPVTC/h+poKz1ThhoYq2ZFup/Ao8BLwvwB/OGmnMWZ/poGNMRER+S7we+yezg8aYxpF5Dbn/L3GmE0i8jtsJRMDHjDGbBzgZ1EU5SQjlYP4uoUVdrG6IS4udzwiJlWt2GFKbW2tqa+vH2oxFEUZ5mSqznqyISINxpjaTNdlWyVVURTluEKjiQZGxoxmRVEU5eRBlYKiKIrioUpBURRF8VCloCiKonioUlAURVE8VCkoiqIoHqoUFEVRFA9VCoqiKIqHKgVFURTFQ5WCoiiK4qFKQVEURfFQpaAoiqJ4qFJQFEVRPFQpKIqiKB6qFBRFURQPVQqKoiiKhyoFRVEUxSOnSkFELhORJhHZIiJ3pLnuLBGJisj1uZRHURRFSU/OlIKIhIB7gMuB04GbROT0FNf9HPh9rmRRFEVRsiOXO4WzgS3GmGZjTA/wOHB1wHV/CawG2nMoi6IoipIFuVQKU4FtvtdtzjEPEZkKLAHuTTeQiCwXkXoRqd+zZ8+gC6ooiqLY5FIpSMAxk/D6buD7xphouoGMMauMMbXGmNqJEycOlnyKoihKAuEcjt0GTPO9rgB2JFxTCzwuIgClwBUiEjHGPJtDuRRFUZQU5FIprAPmiMhMYDtwI7DUf4ExZqb7s4g8BLygCkFRFGXoyJlSMMZEROS72FFFIeBBY0yjiNzmnE/rR1AURVHyTy53ChhjXgReTDgWqAyMMd/KpSyKoihKZjSjWVEURfFQpaAoiqJ4qFJQFEVRPFQpKIqiKB6qFBRFURQPVQqKoiiKhyoFRVEUxUOVgqIoiuKhSkFRFEXxUKWgKIqieKhSUBRFUTxUKSiKoigeqhQURVEUD1UKiqIoiocqBUVRFMVDlYKiKIrioUpBURRF8VCloCiKonjkVCmIyGUi0iQiW0TkjoDzN4vIR86/d0TkjFzKoyiKoqQnZ0pBRELAPcDlwOnATSJyesJlnwMXGGO+BPwIWJUreRRFUZTM5HKncDawxRjTbIzpAR4HrvZfYIx5xxjT4bysAypyKI+iKIqSgVwqhanANt/rNudYKr4DvBR0QkSWi0i9iNTv2bNnEEVUFEVR/ORSKUjAMRN4ochF2Erh+0HnjTGrjDG1xpjaiRMnDqKIiqIoip9wDsduA6b5XlcAOxIvEpEvAQ8Alxtj9uVQHkVRFCUDudwprAPmiMhMESkEbgSe918gIpXA08CfGWM251AWRVEUJQtytlMwxkRE5LvA74EQ8KAxplFEbnPO3wusACYAvxYRgIgxpjZXMimKoijpEWMCzfzDltraWlNfXz/UYiiKohxXiEhDNotuzWhWFEVRPFQpKIqiKB6qFBRFURQPVQqKoiiKhyoFRVEUxUOVgqIoiuKhSkFRFEXxUKWgKIqieKhSUBRFUTxUKSiKoigeqhQURVEUD1UKiqIoiocqBUVRTigaWjq4Z80WGlo6Ml+sJJHLJjuKoih5paGlg5sfqKMnEqMwbPHIssXUTC8ZarGOK3SnoCjKCUNd8z56IjFiBnojMeqatZljf1GloCjKCcPiqgkUhi1CAgVhi8VVE4ZapOMONR8pinLCUDO9hEeWLaaueR+Lqyao6WgAqFJQFOWEomZ6iSqDYyCn5iMRuUxEmkRki4jcEXBeROSfnfMficjCXMqjKIqipCdnOwURCQH3AJcCbcA6EXneGPOx77LLgTnOv0XAvzr/H3Rm3PGf3s9bf/bHuXgLRckZDS0dahJR8kIudwpnA1uMMc3GmB7gceDqhGuuBh42NnXAeBGZPNiC+BVC0GtFGc64YZa/fLmJmx+o0/h7JafkUilMBbb5Xrc5x/p7DSKyXETqRaR+z549gy6oogxnNMxSySe5VAoScMwM4BqMMauMMbXGmNqJEycOinCKcrygYZZKPsll9FEbMM33ugLYMYBrjpmtP/tj9Skoxy0aZqnkk1wqhXXAHBGZCWwHbgSWJlzzPPBdEXkc28F80BizMxfCqCJQjmc0zFLJFzlTCsaYiIh8F/g9EAIeNMY0ishtzvl7gReBK4AtQBfw57mSR1EURclMTpPXjDEvYk/8/mP3+n42wO25lEFRFEXJHq19pCiKonioUlAURVE8VCkoiqIoHqoUFEVRFA+xfb3HDyKyB2gZ4O2lwN5BFCcfqMz5QWXODypzfgiSeboxJmP273GnFI4FEak3xtQOtRz9QWXODypzflCZ88OxyKzmI0VRFMVDlYKiKIricbIphVVDLcAAUJnzg8qcH1Tm/DBgmU8qn4KiKIqSnpNtp6AoiqKkQZWCoiiK4nFCKgURuUxEmkRki4jcEXBeROSfnfMficjCoZAzQaZMMp8mIu+KSLeI/O1QyJhIFjLf7Dzfj0TkHRE5YyjkTJApk8xXO/J+4HT7O3co5EyQKa3MvuvOEpGoiFyfT/lSyJLpOV8oIged5/yBiKwYCjkTZMr4nB25PxCRRhF5M98yBsiT6Tn/ne8Zb3T+Pk5JO6gx5oT6h12m+zOgCigEPgROT7jmCuAl7M5vi4H3jgOZJwFnAT8B/vY4ec5fBkqcny8/Tp7zGPp8bV8CPhnuMvuuex27KvH1w11m4ELghaGUcwAyjwc+Biqd15OGu8wJ138deD3TuCfiTuFsYIsxptkY0wM8DlydcM3VwMPGpg4YLyKT8y2oj4wyG2PajTHrgN6hEDCAbGR+xxjjdpmvw+6sN5RkI/MXxvkGAaMJaA+bZ7L5ewb4S2A10J5P4VKQrczDiWxkXgo8bYxpBfs7mWcZE+nvc74JeCzToCeiUpgKbPO9bnOO9feafDLc5MmG/sr8Hezd2VCSlcwiskREPgH+E/h2nmRLRUaZRWQqsAS4l+FBtn8b54jIhyLykohU50e0lGQj86lAiYi8ISINIvLNvEkXTNbfQREZBVyGvXBIS06b7AwREnAscbWXzTX5ZLjJkw1ZyywiF2ErhaG2z2clszHmGeAZETkf+BFwSa4FS0M2Mt8NfN8YExUJujzvZCPzeuxaPF+IyBXAs8CcXAuWhmxkDgM1wMXASOBdEakzxmzOtXAp6M+88XXgD8aY/ZkGPRGVQhswzfe6AtgxgGvyyXCTJxuykllEvgQ8AFxujNmXJ9lS0a/nbIxZKyKzRKTUGDNUBdGykbkWeNxRCKXAFSISMcY8mxcJk8koszHmkO/nF0Xk18fBc24D9hpjDgOHRWQtcAYwVEqhP3/PN5KF6Qg4IR3NYaAZmEmf86U64Zo/Jt7R/P5wl9l37V0MD0dzNs+5Erv/9peHWt5+yDybPkfzQmC7+3q4ypxw/UMMvaM5m+dc7nvOZwOtw/05A/OA15xrRwEbgfnDWWbnunHAfmB0NuOecDsFY0xERL4L/B7bO/+gMaZRRG5zzt+LHaFxBfaE1QX8+VDJ68iUUWYRKQfqgbFATES+hx1pcCjVuEMtM7ACmAD82lnFRswQVpvMUubrgG+KSC9wBLjBON+sYSzzsCJLma8H/kJEItjP+cbh/pyNMZtE5HfAR0AMeMAYs3E4y+xcugR42dg7nIxomQtFURTF40SMPlIURVEGiCoFRVEUxUOVgqIoiuKhSkFRFEXxUKWgKIqieKhSUPKOiMwQkSEL5fPjVL18wfn5qgxVSM90sm9Tna8VkX/O8H4/GLi0cePMEJGlac7PEZEXROQzpyTDGidD2z1/jVMN9hMR2SAi1/jOPSQinzuVNdeLyDmDIbNyfKBKQVEcjDHPG2N+luaSM7HzW5IQkbAxpt4Y81cZ3mZQlAIwA7tAW5AsI7DrNq0yxswyxtRgF8yrcs6fAfwCuNoYcxpwFfALJ/vc5e+MMWcCdwD3DZLMynGAKgVlqAiJyP1OXfqXRWQkeKvxOmcV+4yIlDjH3xCRfxKRtSKyyekd8LSIfCoiP3YHFZE/FZH3nVXufSISSnxjpwb9JyLyNnCt7/i3RORfnJ+/4dSf/9B5z0JgJXCDM/YNInKXiKwSkZeBhxN2HWNE5H87q/CPROQ6EfkZMNK5/5EAub4QkV86q/PXRGSic3y2iLzqyLJeRGYBPwPOc8b6HwlD3Qy8a4x53j1gjNlojHnIefm3wE+NMZ875z4H/hfwdwG/p7XYWd7KSYIqBWWomAPcY4ypBg5gZxIDPIxd3O1LwAbgH3z39BhjzseuBvoccDswH/iWiEwQkXnADcBXnFVuFHuC9HBW0fdjFwg7D7vcQhArgD8yxpwBXGXs0sQrgCeMMWcaY55wrqvBXnEnrtrvBA4aYxY4n+V1Y8wdwBHn/ptJZjSw3hizEHjT99kfcZ7VGdg9KnZir+Dfcsb6p4RxqrELzqWiGmhIOFbvHE/k69i/B+UkQZWCMlR8boz5wPm5AZghIuOA8cYYt6PVvwPn++5xV74bgEZjzE5jTDd2/Zdp2NUra4B1IvKB87oq4X1Pc977U6eswn+kkO8PwEMicgt2CYFUPG+MORJw/BLgHveF6esrkY4Y4Cqb/wDOFZFiYKqxK7dijDlqjOnKYiwPZ8e1UUSedg8RXDnYf+wfnWe4HLvCrXKScMLVPlKOG7p9P0exSxFne08s4f4Y9t+yAP9ujPn7DONkrO1ijLlNRBZhF0/8QETOTHFpqnoyQRNvfzEEl0fORCM+ZWqMWSIitdh+BPd8LXYNH5eF2F3FXP7OGPPUAN5bOc7RnYIybDDGHAQ6ROQ859CfYZtRsuU14HoRmQQgIqeIyPSEaz4BZjp2ebC7USUhIrOMMe8ZY1YAe7F3Ip1AcZayvAx81zdeifNjr4gUpLjHwi4UB7YT+W2n4GGbGx0kIkViN0xJJ8ujwFdE5CrfsVG+n38B/L2IzHDGnIHtAP9lVp9MOaFRpaAMN/4PbNPFR9jRPiuzvdEY8zHwQ+Bl5/5XgMkJ1xzFNon8p+Nobkkx3D86TuKN2M7WD4E1wOmuozmDOD/G7tK1UUQ+BC5yjq8CPgpyNGPvOqpFpAH4Kn2f/c+Av3I+0zvYfpCPgIjjfI5zNDvmrCuB20SkWUTedZ7Lj53zHwDfB34rdoe53wL/l8+cp5zEaJVURRkmiMgXxpgxQy2HcnKjOwVFURTFQ3cKiqIoiofuFBRFURQPVQqKoiiKhyoFRVEUxUOVgqIoiuKhSkFRFEXx+P8BmW6axsLNn2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k5fW1LHqYOPEpxXUNIwlJdhxP4mWKrwLS5R2OYQxNitlFlMJRHScS5gA3BookwjFKVW8t+0gMYwgTdk4DvLP9QOB7cMQ5xPtKWCRqR9UEmdv5rluOirZWJbcySSIcT4nIZar6dNlHYxhDFOecvu2JdaQVnt24m5e27OWBGz2TVTkqyrrzk1y3HPe3KrmVS5Koqu/hicdRETkkIodF5FC5B2YYQw3POd3zOlxHKld9qXBkVF9IWreqHH3KB7v3uTF4FBQOVR2tqilVPUFVT/Jfn5Tk4iJyiYhsFpEWEflhzOciIj/zP39XROYUce5fi4iKyClJxmIY5aZxxjhGVEnwOuygrh1VQ0qEVKgAYinCcpMWVuxrAcY4nNjVjqop2TWNoUVOU5WInK2q74Un8zCqujbfhUWkCq93x8VAO7BaRFaq6sbQYZcCM/2f+cDPgfmFzhWRqf5n25I9pmGUn7nTanlg8fmsWNuOQNAwyRUxTGeUqpQEFXDverGl32G5SR3ipXKcR81TSxbW03Gk03wcFUY+H8d/AhYDP4n5TIGvFrj2PKBFVVsBRORBYBEQFo5FwL2qqkCTiIwVkYl4ob/5zv2fwN/gRXwZxnFDnHPamXQUUFU6jnQCpQnLzXXP/hyXj6h5quNIJzcvOLNf1zSGHjmFQ1UX+38u6OO1JwPbQ6/b8XYVhY6ZnO9cEflj4CNVfUdEyIWILMYTPurq6vr2BIZRAnIJxPEcPpsrWspyRQxIlgD4KeAvgS/h7TReBZap6ieFTo15TxMeE/u+iIwCfgz8uwL3RlXvBu4GaGhoiN7XMAaMfAJxPNaPuv/NbSx5cj0Z1dhKu8er2BkDR5Jw3HuBw8C/+K+/Bfz/wJUFzmsHpoZeTwF2JDymJsf7ZwCnA263MQVYKyLzVHVXgmcxjEHheBSIOJrbOljy5Hq6/fCwzhyVdvPlpxjDnyTCMUtVPxd6/aKIvJPgvNXATBE5HfgIuBq4JnLMSuAW34cxHzioqjtFZG/cuaq6AZjgThaRrUCDqu5LMB7DGJaUcuJuat1POhRTnBLJa46yXI7KJIlw/JuINKpqE4CIzAdeL3SSqnaLyC3Ab4Eq4B5V3SAiN/mfLwOeBi4DWoAjwHfynVv00xnGMCMqEqWeuMOtZlMpYemi2WUv2GgMPfKF467D8zeMAK4TkW3+62lkR0blxM82fzry3rLQ7wrcnPTcmGOmJxmHYQwH4kQi38Td3NbRKzS4EMX6MBpnjKM6JXSlvVBjc5ZXBvl2HAsHbBSGYQD5zU5Nrfs51uWF9XZ25a+I29zWwbfufoPOtGd2eqS5PSh/Uogk/hg3ztpRNV6YMb0jX4zhS75w3LaBHIhhDAf6428oZHZykzRAxn+da4fQ1LqfrnTPVF5KM1J4nAK423SllcfWtpupqgKwRk6GUQKcWejR5na604ULDsaJSyF/QceRTlICGYWUECQSxu0QXPkTt+MoZc5FeJxRbNdRGZhwGEY/cStwZ0aC3Cv8fLuKQsl1xSTf5Sp/Uopn/ejAUaqrUqTTGapSQgZIp5URVcIVc6b0+x7G8Y8Jh2H0k3BJEfCyV3NN7Pl2FYUc08U6rsM7EVeYsFR1qqpTwtXz6rjcFwrL46gsTDgMox/ErcCvbJgau8KPHhsnLoUc031JJCxVyG5Y9NIZZdLYE7JEz6gcTDgMo0jCEUVLn9rQawVeqJFSoWPj7hVdzce931ffSVKsTpXhMOEwjCII+zNEQNVzCEdX4FHyrdYL3Su6U4h7H8gSpisbplI/aQwdRzqDvhmlqMJrdaoMMOEwjJzEreDDuRTqOzXCzZlyXaMvk3eunUKuznvuvc60ct+b24Kx9advRlyf9GIixYzhiQmHYcSQa7XfOGMcVSkJigAK8JnJY5g9eUzBaxQ7eecyDeV6v6Y6lRXZBV7obmdXhmfW7+T7F51VlGkMBq+fuXF8Y8JhGCHcxLnjwNGsVf2Kte3BhLp00eyg7Hh1Sti06zDrPjrIirXtWZNm0qZHuVbruUxDud6/74ZGVqxt5+E12+kOJf9lgNdb9rF668d5J/WoAFwxZ0oi34jVq6o8TDgMwyfqwA5HSkUT+x767vmBwDzw1rbYSTOJM7nQaj2XaSjuffeeQGCqciSZ1KMCoJDIvGZO88rDhMMwfKIO7KvmTWXy2BNixeHmBWcGjuoVa9tjJ80kzuT+rtbjdiv1k7LNZiOqhExGC07q0YKFV8yZwhVzpgxYP3Nj6GDCYVQESZy30ZXzFX64bD5xALhg5nj2HPqEqz5fV3SDo/6s1nPtVqKlSa5s8AQw0aQuAigZ4LG17Vw+Z0qinuJDpVGVURpMOIxhT1LnbbE+hWgF2k27NjDrtNFF9cnoz2o9127FiVFnV4aUCLMnjWHWaaOD6Kt8pqrutGei6k4r97+5rZffxjDAhMOoAIoxB8WtnPMl1uWqQNvfeyYhLBAA72w/QHNbB3On1bJkYX3gwL995XoQKVh80V0vCDcmO9w3l7hZKG7lYcJhDHvKYQ5y181VgXYgHMZOIG57Yh1phWc37ualLXt54MZGOo50klH1hCvtyUBYCPLtflyVX1cW5fDRLq76xRtkVHt9BxaKW5mYcBjDnlKZg451eWG5YRNWrgq00XsC/S4yGIcnED2vnTCEhasqJSDSqz5W3E7B7X6cU7x2VA1Lnlwf5K10RoTnsbXtwQ7FQnErBxMOoyLoa8azizTqTHsr9ofXbM8SiUKmrZsXnJl3VV7o/knGF7friROuYnqVu+e668UW0iFlSolkCc8ja7YHCYdVVRaKWymYcBgVSxIzy9xptVzZMJX739yW5TR+ZM322Cq40VyQKxumAsSWCHHJeq6XxQOLz895rXzjy7friYqBK68eTXDM5cdonDGOkSN8R3tKWLpodlaCYziD/htzS9Pzwzj+MeEwhiSlcMgmdWBfPmcKK0ImGcWrBxUXdRS+pjtmRJVklVKvHVXDtcub+MR3auNfb0Wk7WrS8SV1rudKcAyPKSpS+cx8taNqSPnhuy7T3KgMTDiMIUdSh2y4wGBcjaikDuyo09hlVcfZ9eMik8LJhI0zxgWCEEUir0vhYA8LbK4Ex+hncQ2mwNshudwOgKVPbSCd8ZIFlyyst91GBWHCYZSVcoRq5qoOG2fDdxN4SghMR2H/RFKnedhpHI06issWjx5zRcSk5cJoM/R0DLw8smLvj1M/rgf6koX1sQmO0TGJCLWjarKuFc5XeWjNdq5qmBp0PVTVoP+5URmYcBhlo9DOoK+iEl2Jx5lZou1cw6ajsHmpkJknWi22qXV/wVIccZFJ4eS7sCDk2g1Fr1UMcT3Qj3V5RRavP386v9mwi0vqT+vl/wjnfix9qieZsal1fyAa4Pl59hw+ZvWpKhgTDqNs5DN/9Cf+P7oSj7tPODnOrepzmZfCRIXiW79s8kJaq4RUJInOleLIV90W4kuTl7NER1Q08Z/7zdb9vPL+PgCWvdJK3bhPc838uuCYrNyPSCZ6lUBIO5gweqTVp6pgTDiMshGevKPmj/4W94tOvNHVb3RVv37HwZzmJUdUzL48c3zgi+hOO9nx+lu48ebr0hdXnn0g8hzc9x52vgO8+9HBrNfPrN+ZJRy5fCpzp9Vy4wUz+MWrrah633XY3GdUHiYcRtEkMTG5Y64/fzrLX/uwl/mjlJnVuXwB0YmtUKXXqJjtPvRJ7P0yEIhgLn9LruilYrPW+7Kid9/Hspc/4LmNu4P3LzxrPE+8vSN4fensibHnRe95/5vbWP7ah6Cen+j2r5sjvNIx4TCKIomJKXyM0GPiCK+4++P4jSPJ6rfQMVExu+rzdWzYsY5oAFRKCJzB4ZBUJwz5opeSPqdzSHdFcjySisncabX88roG7n9zG8+s38mlsycy67TR/J91O4NrzjptdMHvqLmtIytzPGOOcAMTDqNIkpiYwseEiWYWD6apI1e5jaiYbdhxMEj+A89XUuMLxP1vbmPJk+tJZ5SUwJdnjgdyl2cvdP8wK9a2Bw5pl+MB8f6SfNe6Zn5dYI4KZ4Gn08qdq7YUbCfb1Lo/Z+a4UbmYcBhFkcTEFM1lgMHLLI7L5YDcvbSjYuaS/1zNJxfOC2StxNMKz23czSvv7+W+GxqDkNxoboYbU6FdW/Q8obBZzF0L4ivZRgMG8rWTdeG8+w4fY0R1iu7u3pnjRuVSVuEQkUuAnwJVwHJVvSPyufifXwYcAa5X1bX5zhWRfwK+DnQCHwDfUdUD5XwOo4ckJqZ8uQwDSVwuRzG9tMPPEn3eaA0n8FznrhDiFXOm8Njadjr9fuX5epHH3f/yOVN4pLmneZQTq6hoR6/lkvRyieJ9NzRy56otvN6yL+f9o3kb1VXCt+bX9SqvYlQuZRMOEakC7gIuBtqB1SKyUlU3hg67FJjp/8wHfg7ML3Duc8B/UdVuEfkH4L8At5brOYzeuFW5q3uUJJdhMEI243I58vXSzhdWGx1744xxjKhO0dmdwS8+Szrjicejzd5KPVfV2CS7trnTanngxt6CFSdi4WsJBPcNR3+Fr/v9i85i9daPc94/2mcknVYmjT3BRMMIKOeOYx7QoqqtACLyILAICAvHIuBeVVWgSUTGishEYHquc1X12dD5TcA3yvgMw5b+ZnQndZL3VzSKuUb02KhpJiUEO59oct7mXYdZ+tSG4HmWLKzPm5gHgCqCF2l04awJPLdxt1cIsTvD85t295jpUsKOA0eDJktJAwNyCXTc7sQVONy863CPUEJWCHT4uvnu7zn8e4Iaqqu8UOpylIU3hiblFI7JwPbQ63a8XUWhYyYnPBfgz4GH4m4uIouBxQB1dXVxh1Qs/Um+C+cn5FvZxlWJjaskW6ikeHic+SbzXM9UKEPbnZMSCZLfOrsyQQZ1ru/HVYZ1tahOGT2SkSO8lb/413KowgNvFZexXui53GcuQdGZszbs6MnVCEd/Rcl1/+a2Dq8GVcgKl8kotz25Hs3zfRiVRTmFI84vqAmPKXiuiPwY6Abui7u5qt4N3A3Q0NAQvW9F09fku/AklhLyrmzjqsSGJ84kPSrCyXOfdGW47cn1ZPyieksXzc5KXsv1TPkm6PA5qoqIN9mGRSTX9xMXORXexSx9akNgFnO+EOeDKGYXlvU9dmWyIqGcLwO8su2/ePkDXtq8Jzi3Okd/jHyCHVeA0ct9jG/kZFQm5RSOdmBq6PUUYEfCY2rynSsi3wYWAl/zzVxGEcTlHiQhe6LteT9uZZurf7WbOHNlVLsQ14wq1SkhlRIy/vLXTcDdGWXJk+uDZMLw/Vz0U9g0lIuoKUvVe5YvnDGO332wn4x6O4rDR7t6nZsv6RBg1mmjeyXgIWQVHUyyco+LhHqzdT9XNkxl7+FjWcfuPvRJwf4YhQT7owNHqa7yoqjCpVocFo5rAKTKeO3VwEwROV1EaoCrgZWRY1YC14lHI3BQVXfmO9ePtroV+GNVPVLG8Q9LAlNERkmJcP3502lq3U9zW0fec+56sYXaUTXUVKeoEhhRJdRUp0jhTSaHj3bxo8fX8ePH1wUT9n03NPKt+XXBOVUp4dHmdn7y7GYeWbOd6ir/Wr54Nbd1cJsf4ppRTyDqJ54Uu/3MqAbhqI7L50zha+ecCiI88NY2rl3elPe53Bi/OPOU4B4ZhVfe3xdKePPqOt3/5ras7+NHj6/jsbXtOc1mTa372RPJPB9/4ki6073Dad33GzdWN8bPTBkTjMft4F54b7fnmAdqqoSrPt/zXY8cER/Fliuk1wnKg29tA1UuOvdU5k2v5Yzxn2ZElZDC8+VYOK4BZdxx+FFPtwC/xQupvUdVN4jITf7ny4Cn8UJxW/DCcb+T71z/0v8KjASe86J5aVLVm8r1HMOBuJ4Mime7duVAkmSBR/0Mm3cdDhLglr3SGpzzSHM7D9zYY8t3JpwdB47ywFvbvEkrrXx2yknMnjyG+kljaGrdzzvbD2SFuIp4k+Hm3Z7ZBzyzGNqThBcdYxIzUxgXZfS7ln1Zdv0orq5TNFQ1/Ky9x5J9jcYZ4/jNhl1Z0UzRHdaVDVOpnzSGjiOdgV+mdlQNm3YeyrqWQpDRXp0S/vyLp9NxpLOgUz9XRFdT6/6eKLC08vym3VnOcQvHNcKUNY9DVZ/GE4fwe8tCvytwc9Jz/ffPLPEwhzVxE7+bOCTBJBtdoXYc6eTmBWdy/5vbuPuVD0j7DuIwcY2AnBnK6YIC77YfZNPOQzwinvkmurP46tkTAj/GQ6u3sXHnITJpz8dx/fnTg5wF6GnNiiqplCBFmOHmTqvla+ecyrNhs1IEV9cpWmI8+qzh70uAz0/3/Dmq8JsNu3oJ798+sS74TjrTyn2hnQ1k+1wcVSkhE/rekywAws8aZ2KrHVUTXE/JroTbne65V67IqnL0XTGOXyxzfJgTN/GHI41u//UGv2x470k2bPMOF+m7/81t/OjxdTnvGZ2ww87usM3crW7Bm5hSEJTvrhJYMGtCYFoLZ6GnM8ovXmkNXnt5FIL4onHDl05n9AkjiprEvvuVM3jhvd2xdakWXzAjELCovyOVyrb5R1f0Z5062hMOsoXX1YCKlmWJEieG158/nTda97N+xyEyGUVSxe+yop93HOkkJQSCFw7HBXh4zfac/pn+ROkZQxMTjmFOnGkinB8QeLkjMQbRcNqr59UFJqVnN+zKOva0k0ay/w+ddPm7gdu/Xg8Q+EVcfkR1SoLyFS6votrPnnPCFK6mu+TJdZxy4sgs0YDeoXlucgVPVH71xlaWLKzPap4UJbpCnjutloe++4WgTIgzF4XFp7mtw6sSGybyvUVX9EBQssQ1nbrrxRZ2HDjaK/M8DgFqRvSYCN336b4TAQSlqo8VeB3RfydLFtbz0OptvNPuhfem00raF/h8u6yBKh1vDC4mHMOcaC5DeDKN5iLknAz8jm8rfAGI2u4/O2UsqzZ5Zh7NKA+t3samXYfpTmf7HMKVYqO1o8I+GHd8RmHXIS9ySPAc8udMPIl32w/2Eg+Hyyu5zTcBhSvLOnKtkAvlVkQL/rn7xWVnh1+78iv7Dh/j9l9voDvdI6IuZDeOqhRc/fm6oNzIspc/YF37gaw+G04zvzF3StEVeMPkMmG9074uuM+IKs9EFhWnxhnjqE5JsHCwqKvhjwlHBTB3Wm3gyA7bwRtnjKO6KhWYqsLZwW4y6PTt2y+8twf1J3QBLj73VD7pSge2/+BzCFapQC8zS1ylWDdGh7tvmGnjRrH4y2ewfsdBNu063GvCdSawlP+LO70zrSx7+QPOmzo2mBCjuRFLf72B2ZPHFHT+hv0A7p41CVf4j61t72Vuu2reVLZ/fITX3t+XVQzy89NrmXnq6GA8zW0dXPWL3/Uyo7nj832v/SFsvkoJXNmQpzy8H97t/WkMd0w4KoBoT4VwIT78XIXu7gz/daUXIeWE5cJZEwKHsWY8AUAVEWHBrAlBlNG1y5tIZ7wEumhWTXV1itu/Xrh8R9h0dGXD1F5O4vOmjs2KPrpmfl1s9NH6HQf9kNKec194bw/Pb9qdJZjh3Ih32g/yTvtBHlqznatiMtwdHUc6A4ES4EszTylYlhx618wKT/YAq7d+TGdXT/XZcGKjOz+XaCQdQxzh7zzXwqJQefhgfGk/2TFtpqpKwISjAoiaWBQvEU0gWNlnIEi0c4l64SzkVJUwZ+pYmrcdyOrmF54UxV+Zulu5JLTwROjKdQtkraijkV+fGuElDwKcfdpofv3uzqwEwEljT+g1wYLnVwmLl9A7c3vy2BN62fDBix5yGe5xYa2NM8YFZUVGVKcST9jR5MQLZ01g/OiRQLJqw7Wjakj5f0fh5xo5IvkYokTDgNPa8z257PCbF5yZqKZWKbs5GkMDE44KwE14WaaSdIY9kcxj6FkNCwQ7FPB2Equ3dgTnOydodNIIO7drqrOT0HLlQOSK/Fr28ge88N4eNu06nDXGfNnL7lm9PufeA6X9GVf8BETnY4hZxAel0f/2Cc+2H82tKFQvK64uVtTP5IIForWrcl1r6VMbgqizGRNO5KKzJxQdNRa9dngH2pXODqkOf79JamolLdpoDB9MOCoA9x872h8jao2uErh6Xo8z9pE12+lKe93tVLMnF+cEjZs0Lq4/rVfl2Y4jnbyz/UCvHAjX2c75WsIlPl58b08vZ7SrU5VvcnLVYsErLug4dfRIPjrgZXNHfSgnjxrB4WPdpNMalB9xxzmzmevnkS9RMtr7I+p4v+vFloIRSLla76YE/vSPJnPzgv6lMsU5+UdUCem09rlZU9KijcbwwISjAgibh27/ej3rdxxk3+FjvBAyRQlw+vgTqZ/klbZYsbY9qyx4lUgQRgue2cQl34UjsRwfHTjKT59/PxCDaM0j/NePrNnu+UfoiRBa9korrfv+EBuuOqduLB1HOmPrUIV3NFUCN14wI8vR7kQjjgNHu6hOCZ+ZMiZn1Fa+xkdLf70hK9op17H5zDpxxR2zEIktk14s4V4i4L53texwIzEmHMOc+9/cxm1PrMtatValJCsbGLzJo2XP7/nR4+uoSvWYd8BzjH/DjwByneO6/ZX4Q6u3c8OXTudXb2wNcjUQ6RX1lCvktCsd/8nuQ58wckQqazIGz1y2ZmtHbIXccJ/utMLdr7ZyUYGMcOdLceHC9ZPHsHn34cDUlUpJsAtx/Tyik33Y/OZwx0b7WOQy60RLlcSleKQzPb6l/mZuZzLZ32u334TKRMNIggnHMKa5rSNLNMCbkDI5JmtHWDSiEUBvRkpudEeyuDv9TPBi8MpqkBU5dNXnPUEI2+IdSnaFXPB2O/siPpuM5u5H4XDCJHjZ57MnjWH2pDE8s34nl86eGAQAuIitsHmvua2DO1dt6SV+F597KudNHZvlz4jW+Yqam3JVHo4SLkwYTjIsJnPbM1X1fj/6/RlGLkw4hjEr1rbnLdyXhDMmnMg/XPHZYCKKC5WNu4XLCr+yYSqjR1az/LUPewkAeA7fL57phZRu3nU4mLCvmV/Hjx9flze7OqNejsaL7+0JooOiIcHOTxB3lXBorYh3vdtXrgcRutMZVm/9mPtuaAxKhISd2ksW1vcqhQKeX+Omr5zRy59RqDlUNPLKZdO7KKyXtuwNfFO1o2qyhKKYHurQu8Of44XNewqWojcMMOEY1hRKxXIluUdUp/js5DG8tbWj1zHbPvYq1ztTSP2kMYF5J58m1Z3sJexdM7+Ou15sySkAInDCiKrAge7CS5vbOnhkzfZe96hKeU+mvlC8EHKgd6WVM8Z/mtZ9f0DVey5XXdflbAieqc6Z13oVewzVzgpPwtHIr2fW7wzCkFPAZ6aM6ZVEGBaDQgUl40qVhE1PcRWOC/VQj6O5rYPbf+11+EsJjPt0DXt/7+3KutPKirXtJhxGQUw4hjGXz5nCg6u3xZolAC4651Q+52dUb951OFY4urozLHv5A17evIeutDKiSrj9j2ezfsdB3vrwY1r2/D722m37jwT2+GjGNfiNYPxV77Mbd/Psxt1ZkUiuHEoUzcBn/HLsQFafDAVa9/2hV6vasLkpHCYbjv7qKfYopEK1s9wkHHVqXzp7Iqu3ftxT2+nr9b0m3Lgw3HyTe1xkUrhETPizXN0HC/k4wl0DMwpjRvUIh/9X0mesQm7lYMIx7OltqHG7jO/6JhXwJqjwkc5Bq8Dzm3Znlf5+/N/aWffRwSBBLw6XD+GiuaLMmHAiH0REJ7waD3cpdA53F9X1bvtBNuw4xA1fOj3I2cA3UTkn96SxJ/Qq6+5wjZOcryFc7DEFsZnucU5tJ0j5JsrwvZMcHx5jLr9FLgd7ksk6KsW1o0YEZdpd3/K+YBVyKwsTjmHMY2vbs0xErkR4NHnMlU+vqvKiraJEnbVrtnZ4vgTC/oHe93fhtgtmTch6PyXwwZ7fxzagFxHe332Yp/xM8aqUt8OZddpo7ly1Jajr1J1R7n61NXiewBHd5ZmFakfVxK6A4ya4aLFHV/o8SlSAis1dKOb4QhVn+5o3ccWcKTzq5+dUpbxyK66P++3+rqkvOwerkFtZmHAMU5yPwFGVEm70+1SEq+QCQThp1s5APee2qjephLOLvQQ3oSrUHyKX87s7rZwyeiQ1VdKTTEiP6LidghOg7ozyxNs9rekzqkFk1NSTR3nhsaG2rstf+5CHvns+ABfMHM8LvqN8yZPrUCSYFF3obniCC9fsOt5KZpSrjMfcabU8sPj8oOPicxt3B3kcLj+mLzsHKztSWZhwDFPCPgLB66b3qze2ZvVxqEoJc+rGBuG14WlffAfyoWPdCPD+7mwfyNfOnhD4R1wp9DiiNviwrV+kRwRc8l+UlL97COc4hCOnMqpBJ8Cww967rPcqHLobrfr7aLMnHKUumdFfe385y3i4a/101Zbg+3KNvPq6c7CyI5WFCccwJboCnDB6ZFaFVmfuWR3jEAfPZLP89Q9J+aGp1SlhhG/KivpHwHPWRiOtzjltNHNi/AxhZ3U0zyRMSmDpotl0HOnMasd60TmnBiG4KYEXNu0uGOWVUQ0K913ZMJX739yWVc315gVn5k2qA0rinyiGcpbxiAYfnOPnw/Rn52BlRyoHE45hSlx454q17UFYaphcmcrdaUVcaGpauchPbItOnu5eYR+EAO/v+T2bdx/OKubnjne/v7R5T2xmtxMIl+AXnsxu+soZ3PSVM1j28gc8t3F3r2ZPLu8hXO4k3Dfj8jlTsrryxU2O0Q6ILrcjaYJdf+395Y5QipaWX/fRQa5d3sR9NzTazsEoiAnHMCa6AnQTwuGjXVkVbJcsrOfFzXtY5du7HSm/5IYrT/Lylr3c9JUzALLKaLh7ff+is4IQ1XDegvMlRCObmlr3c+GsCbyweU+WU96VRVm1aTevvL8352T2SVc663mnjRvFT755XpaDNxqC68bqij7mCj+NdkCMy+3IRX9W7c1tHUFVYA39/bis9VLVkgqLvSsj454tbveVdOwmOJWBCUcFERaSunGfzsrS7jjSyarIyv+ic04FCByo6XQm8CfEldGI5i3cvnJ9L1+Cm9TDppylfl7IvsPHgj4VD7y1Le9k1tzWwadGVGWNd/GXz4jd1YQJC4p7jkfWbM/K+4DcmdxJhKCv9v64ulfH/Da47i1Xir5U4uHE3pn6XGXiYnFjd7k+0Xa9xvDChCMPQ3kFlW/s4fIZq7d+HDiNw30sUv6K3/XGDpdiL1RGw91vw46DvXwJcVnYHUc6+fs//UzW+PKZksIlzFPAxNoTqJ94UmDWyvV9uLLy0V7onaEGTuFnyJfJXYi+2PubWvf3qnsVDXUudajr3Gm1XH/+dJa90hpUJq4b9+nYJln5CBeY7LQM9GGPCUcOhnJCU3hijasiG2eDD3d723HgaLDiT2eUr54zgT2HPuHUkz5F/aQxicto5PIlFDLlFFqxN7XuD1bICnzUcZSdB44GZq04oQz3ygCCXuiuz0icGSoub6O/5BP0xhnjGFHVUwbehVDf8/qHwXvlCHXdsPNQ1utn1u8sWjiiJr/+ZKAbxz8mHDkYyglN4Yk1HIoaNcOEk+WgZ6IMr/irUsKLgQ/iIC9t2RtkVhcqo5Evw7mQKSffir1xxjivNHxoKR739xTtb+GOFqBmhJd/8stXWwMzkAtJLReFFiMuxyLaWvfi+tN6vVdKLp09kVff35f1ulgunzOFR5p7Fgl9zUA3hgYmHDkYyglNtaNqeuU6RFfSSxbWB2amaI+H8MS+48DRrHpQzrTkMqsLldHIJQBhkYo62gsxd1otSxfNZsmT60n7Gd8uL8X9PUWjoqqrUkG1WefPaGrdnyUm35hb3iZGSRYjcd9XucNc3e4i7PMqlrnTaoM2wEPRtGsUhwlHDoZqQpPzX7jFuOBlgO84cDSrZHbHkc68ZiZH/aQxWeaTqIj2Z1Lrjznwmvl1zDptNCvWtvPImu3ejkh6DCThSTqdUa6aN5XJY0/o9XcZLRZYTo7nxcg18+v6JBhh5k6rZfOuw9y5akufBcgYGphw5GEoJjS5CRN6yn1v2nWYB97Kdv4WamEantBdNdz+mkqi9v3+mgPdNdyuI+yAjz7fFTHjHujFwVBdjCTl/je38aPH1wHw6vv7eOvD/dx59R8N8qiMcmDCMcyITpizJ49h3UcHe03O+Sax8ITe2eX1nnB9MvpK3O6iFCvwXNdIOkkP9OJgKC5GkvLM+p1Zr594ewfzTh9nO49hiAnHMCOc3Lbv8DH2Hj4W2Pdz9cCOEs0qfr1lX9ANr6+TXqFIrnLUdBrOk/TxSP3Ek7Kc7NC3CC3j+KeswiEilwA/BaqA5ap6R+Rz8T+/DDgCXK+qa/OdKyInAw8B04GtwDdVNb7gUgXz6JrtgV+iukq4el4d9ZPGZPXAziUE+bKK+zoR59sZ9HdyN4E4Phh9wohe7/UlQss4/imbcIhIFXAXcDHQDqwWkZWqujF02KXATP9nPvBzYH6Bc38IPK+qd4jID/3Xt5bjGZrbOrjjmU1s+OggXRklo4r6LTddNVcvqVhK8lmprpPR7KSxdNprbBQuFlhICMJZxaVw5g53+77hLQ4+NSLFJ36DryqB255cx98+sa6k/74H4v/QcBnbiKoUsyedxK2XnlPS/3Pl3HHMA1pUtRVARB4EFgFh4VgE3KuqCjSJyFgRmYi3m8h17iLgQv/8/w28RBmEo7mtg28u+11s5dbwpBwu393/z0p1nWyqq3rCVIvxKZR6sredwfAmnIUOXltg90+zHP++B+L/0FAfW1c6zVtbO/jmL97g4e+WrgxMOYVjMrA99Lodb1dR6JjJBc49VVV3AqjqThHJbi/nIyKLgcUAdXXF21ibWvfnLPc91LiyYWrwD6ZYIbDJ3iiGaBa6cXyQzmi/TM1RUiW5SjxxVQeiU3GuY5KcmxdVvVtVG1S1Yfz48cWcCvjZycOgbkJVSrLyE+ZOq+1z9VPDKIT5NI5PwsmxpaCcO452YGro9RRgR8JjavKcu1tEJvq7jYnAnpKO2mfutFoevukLQ9LHUU7bpmHkw0VQ3fXi++z7fSfdmcyw9yMcz2Mr1zwg6upSlBgRqQa2AF8DPgJWA9eo6obQMf8vcAteVNV84GeqOi/fuSLyT8D+kHP8ZFX9m3xjaWho0DVr1pT+IQ3DMIYxItKsqg3R98u241DVbhG5BfgtXkjtPf7Ef5P/+TLgaTzRaMELx/1OvnP9S98BPCwifwFsA64s1zMYhmEYvSnbjuN4wnYchmEYxZNrx1FO57hhGIYxDDHhMAzDMIrChMMwDMMoChMOwzAMoygqwjkuInuBtj6efgqwr+BRwwt75srAnrky6M8zT1PVXhnUFSEc/UFE1sRFFQxn7JkrA3vmyqAcz2ymKsMwDKMoTDgMwzCMojDhKMzdgz2AQcCeuTKwZ64MSv7M5uMwDMMwisJ2HIZhGEZRmHAYhmEYRWHC4SMil4jIZhFp8cu1Rz8XEfmZ//m7IjJnMMZZShI887X+s74rIr8Tkc8NxjhLSaFnDh33eRFJi8g3BnJ85SDJM4vIhSLytohsEJGXB3qMpSbBv+0xIvJrEXnHf+bvDMY4S4WI3CMie0RkfY7PSzt/qWrF/+CVbv8AmIHXROod4NzIMZcBz+B1J2wE3hzscQ/AM38BqPV/v7QSnjl03At4Zf+/MdjjHoC/57HARqDOfz1hsMc9AM/8I+Af/N/HAx8DNYM99n4885eBOcD6HJ+XdP6yHYfHPKBFVVtVtRN4EFgUOWYRcK96NAFj/Q6EQ5WCz6yqv1PVDv9lE14nxqFMkr9ngL8CVlCm7pIDTJJnvgZ4TFW3AajqUH/uJM+swGgREeBEPOHoHthhlg5VfQXvGXJR0vnLhMNjMrA99Lrdf6/YY4YSxT7PX+CtWIYyBZ9ZRCYDfwosG8BxlZMkf89nAbUi8pKINIvIdQM2uvKQ5Jn/FTgHryX1OuB7qpoZmOENCiWdv8rZc3woITHvReOUkxwzlEj8PCKyAE84vlTWEZWfJM98J3Crqqa9xeiQJ8kzVwNz8Vo1nwC8ISJNqrql3IMrE0me+f8B3ga+CpwBPCcir6rqoTKPbbAo6fxlwuHRDkwNvZ6CtxIp9pihRKLnEZHPAsuBS1V1/wCNrVwkeeYG4EFfNE4BLhORblV9YkBGWHqS/tvep6p/AP4gIq8AnwOGqnAkeebvAHeo5wBoEZEPgbOBtwZmiANOSecvM1V5rAZmisjpIlIDXA2sjByzErjOj05oBA6q6s6BHmgJKfjMIlIHPAb82RBefYYp+MyqerqqTlfV6cCjwF8OYdGAZP+2nwQuEJFqERkFzAc2DfA4S0mSZ96Gt8NCRE4FZgGtAzrKgaWk85ftOABV7RaRW4Df4kVk3KOqG0TkJv/zZXgRNpcBLcARvBXLkCXhMy8BxgH/y1+Bd+sQriya8JmHFUmeWVU3ichvgHeBDLBcVWPDOocCCf+e/w74lYiswzPj3KqqQ7bcuog8AFwInCIi7cB/BUZAeeYvKzliGIZhFIWZqgzDMIyiMOEwDMMwisKEwzAMwygKEw7DMAyjKEw4DMMwjKIw4TAqDhG5XUT+2v99qYhclOfYPxGRc/N8flO+Eh0iMl1ErunfiINrXSgiX8jz+SUi8paIvOdXun3Iz8Vx1VH/VkTeF5EtIvKiiNSHzt0qIuv8arHPishppRizMTwx4TAqGlVdoqqr8hzyJ0CscIhItZ8HcW+e86fjFREsBRfiVSyOG8ts4F+Ab6vq2ap6HnCff3+Am/1zP6eqZwH/A1gpIp8KXWaBqn4OWINXPdYwYjHhMCoCEfmx359hFV6WsHv/V+L33BCRO0Rko9+v4J/91f0fA//kr+DP8AsB/r14PSu+F9m9nCkiq/xV+1oROQO4Ay8r+20R+UFkTBeKyCsi8rh/32UikvI/u8S/xjsi8ryITAduAn7gX+uCyCPeCvy9qgYZ36q60q+a6j7/K1U94n/2LPA74NqYr+sV4Mw+fM1GhWCZ48awR0Tm4pWd+CO8f/NrgebIMSfjVcU9W1VVRMaq6gERWQk8paqP+scBjFXVr/ivbw9d5j68+keP+yv5FPBD4K9VdWGO4c3D29G0Ab8BLvdF6ZfAl1X1QxE5WVU/FpFlwO9V9Z9jrlMPxL2PiJwEfFpVP4h8tMY/L8pCvIqxhhGL7TiMSuAC4HFVPeJXP43WLQI4BHwCLBeRy/HKMuTioegbIjIamKyqjwOo6idudV+At/y+EWngAbwKxI3AK6r6oX+tfH0WeiEi4/xdyRa3G8p1KNkVUl8UkbeBk/BMWYYRiwmHUSnkra2jqt14q/8VeH6N3+Q5/A8x7/W1Bnt0XErvCT0JG/A6wKGq+30fx93Aib5Y/kFEZkTOmYPX+c+xQFXPU9XrVPVAkfc3KggTDqMSeAX4UxE5wd8ZfD16gIicCIxR1aeB7wPn+R8dBkYXuoE/ObeLyJ/41xvpV5otdP48v4prCrgKeA14A/iKiJzuX+vkBGP5R+DHInJO6L1Rod//CfiZiJzgX/MivN3N/YWezTCimHAYwx5VXYtnXnobb0fxasxho4GnRORd4GXAObIfBP6ziPyb7+zOx58B/9G/xu+A0/Aqznb7Tu4fxJzzBp4DfT3wIZ5JbS+wGHhMRN6hxzT2azwB7OUcV9V1wPeAe/1w3NfxOtw5YfgXvHLj60RkM3AbsEhVjxZ4JsPohVXHNYxBQkQuJL/j3DCOS2zHYRiGYRSF7TgMwzCMorAdh2EYhlEUJhyGYRhGUZhwGIZhGEVhwmEYhmEUhQmHYRiGURT/F/dyP0m2KoHYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5LixjTJJDr76KxsVR/el/kbKtztuk8pYETHa4qRqag9N99Fsg3VO/xhRqXrpp5oZTK1ZwfO5cAh94gBL16iWv7//WUtbuyXiuKHtS2GSXqyEmVHWCzyMxJj+aNCnPTq3nz3Pg3y9SvHZtqgy/N3n9nR+uzDQJ2PMB5mK4SQT/FZHncBqJkx9RVNU1PovKGMORKVM4v2MHtSe+i18pZ8awkdN/z7Q6yOYQMBfLTSJoDvwNZ8TRpKohxd0IpMYUbMOHO++5fGcQt28fh995l3JduxIQGgpk3iZQroQ/U+5pa11DzUVzkwgGAA1tjgBTJP35Z56c9uDL40CV6k+NznLMIEsC5lK5GX10HVDRx3EYYzxOLlnCiR9/JHDECGbuSWDgu8szTQJjBzS3JGAumZs7gsuAzSKyitRtBNZ91JgclnjuHAdefIkS9esztlxLZs2OzLTs2AHN7UExkyPcJILnfB6FMQaAIx98QNyuXXx+y2PM+iM603KWBExOcpMIVgNnVDVRRK7AmS9gvm/DMiafaNEi1051fvdujkx6n42N2zA17rIMy9iYQcYX3CSCxUBHEakE/ISTGAZhw06YoiCXRh1VVQ6+NIaziTC2/vUZlrFnBIyvuGksFlU9DdwE/E9VBwDNfBuWMUXLyZ9/5uQvvzD1iu4cKV0h3fYRnRpaEjA+4+aOQDyzlN0O3ONZ5++7kIzJR+64w3n34UxliWfOsP25F9kfcBlfX94x3XZrDzC+5iYRPAKMBmZ7ZhhrCCzybVjG5BN79vj8FL88+yrVDx/k7Q73k+B34TuWPShmcoubYagX47QTJC3vwJmcxhhzidYsXUuV72aysE4r/gi8PNU2SwImt7hpIzDG+ICqsuOZ5znnX5wPm92QatuITg0tCZhcY4nAmDzy7Mg3abZ/C1Oa9uJYqYDk9f1b1LTB40yucjV5vTFFVkiITw77+qw19PhlOlsr1GJegwvnsC6iJi9kmghE5H84o4xmSFWtncAUfi+/nOOHjIiK4exHk6h89gQvtfk7ieLcmLeoXcGSgMkT3u4IVudaFMYUIR9/8iMjti/h+3pt2FLZmXWsekBJm1rS5JlME4GqTgEQkVtUdWbKbSJyi68DMyZfGDjQef/qqxw53LQVUXSaP4WTxUvxcbPeyevfvqNVjhzfmIvhprF4tMt1xhQ+R444rxyy/L1PaX5kBx837cOJEmUB6N70MushZPKUtzaCXkBvoJaIpJyzuDwQ7+vAjClsBo//gYfXzGFTpXr8UK918voRnS/3spcxvuetjWAfTjtBPyAixfoTwD98GZQxhc2dH66k5cIvKH/uFM+E3It6GojteQGTH3hrI1gHrBOR2cApVU0AEBF/oGQuxWdMgTdu3ib2rlrL4zuXM7dBe7ZXrAVAp0aB9ryAyRfctBH8AJROsVwaWOibcIzJZ7p2dV4XKSIqhvd+2caD62YRW7IcU5v0BJyuolPvaZtTURpzSdw8UFZKVU8mLajqSREp48OYjMk/nnnm0nafE0mPqFVcGbOL1669jVMlSlOvchnrKmryFTd3BKdE5NqkBRFpBZxxc3AR6SkiW0Rkm4g8mUmZUBFZKyIbROQXd2Ebk/+Nm7eJ3VEHGLpxLpFVGvJzHee/0RuDWuRtYMak4eaOYCQwU0T2eZZr4MxQ5pWnLeFtoDuwB1glIt+o6sYUZSoC7wA9VXWXiFTLXvjG+FivXs77/OzNzhoRFcPExTt4eMM8ysWd5e1rBoAIretXssZhk++4GYZ6lYhcCTQGBNisqnEujt0G2OYZthoRmQ7cCGxMUWYIMEtVd3nOdSib8RvjW2dc3fym88ycSBofjeL6qN+YHdSJqPI1AHiylzUOm/zH7eijjYGmQEvgNhG508U+tYDdKZb3eNaldAVQSUTCRSQis+OKyHARWS0iq6Ojo12GbEzeGDdvE5v3xfJ/62ZxtFQAnzXuDjgzjdndgMmPsrwjEJHngFCcRDAP6AUsBaZmtWsG69IOYlcMaAV0xemN9KuIrFDVP1PtpDoJmAQQHByc6UB4xuS1E2fjmbhiBzfs/JVGsXsZ2/oOzhQvxYhODW26SZNvubkjuBnng/qAqg4FrsHdcwR7gDoplmvjPKSWtswCVT2lqodxZkK7xsWxjcmXdh89TcWzJ/j7xvmsqdqIJTWvoUn1AHtewORrbhLBGVVNBOJFpDxwCGjoYr9VQCMRaSAiJYDBwDdpynwNdBSRYp4uqW2BTe7DN8bHbrjBeblw4mw8x8/Gcc+G7yiZEMc7VzsNxC8NaO7jII25NG56Da329O55H2eoiZPAb1ntpKrxIvIg8D3gD3ykqhtEZIRn+0RV3SQiC4D1QCLwgar+cXGXYowPjBrluujuo6dpsvcM3XZvZfoVXdkbUM16CZkCwWsiEBEBXlbVY8BEz4d2eVVd7+bgqjoPp10h5bqJaZZfA17LTtDG5DcRUTGcOnWe4eGHOFi6EtOvcJ5Gtl5CpiDwWjWkqgrMSbH8l9skYEyhEBrqvLLwyvxN9F53jHpHzjPx6hs5V6yEDShnCgw3bQQrRKR11sWMKZqmrdzF9k1/MXjlEVbXL8OK6s2sgdgUKG7aCMKA+0QkCjiF0y1UVfVqn0ZmTAHxzqKt3PvHt/glwoedq0Gs0NLuBEwB4m1imgaquhPnuQFjTAbGzdtE4NZIOu9dy+dtK3OwQnEkFgZeWzuvQzPGNW93BF/iPOz1kape/Di8xhRSEVExfLjoT95eN4t9Zaswp1VFALrZ1JOmgPGWCPw8TxVfISKPpt2oqm/4Lixj8olbb8100z+/WMuA7b9Q52Q0T4cMI67YLwg29aQpeLwlgsFAf0+ZgFyJxpj85oEHMlx954crOb17D7dtWcjSGs2JuOxKSvMLDQLL2d2AKXC8TVW5BXhFRNaravbG4DWmsDh92nkvc2EupnHzNrF462GeiXQelH+v+Y0A1KxYmmrlbRZXU/C4GYbakoApunr3dt7Dw4EL8wy0PrCJ9vv/4KOmvTlcpiKdGgXiX9km7jMFk9thqI0xOA+OlUiI4/71c9gVUI3ZQZ2oULqYzT9sCjRLBJdgwoQJ2Sr/0ksvMXny5ByN4a+//uKbb9KO5Zczx+3SpQvXXXcdY8eOzbTcn3/+SfHixVm6dGnyuqlTp9K1a1fCwsKYNm0aALNnz6ZJkyaUKlUqx2PNLdNW7uK3v2K45c+fqXH6CG9ffRPxfsV4oqc9OGYKtiwTgYgUF5GHReRLz+shESmeG8Hld9lNBL7gq0Tw5JNP8sILL7Bs2TJ+/vlnNm/enGG5F198kc6dOycvb9iwgYULF7Jw4UIWLVrEkCFDAOjUqRO///47tWsX3P71M1btosapw9y6dRGLardkfdUgWtevZPMMmALPzR3BuzjPE7zjeV3rWZcvjR49ms6dOxMSEsJ3332HqtKvXz/Cw8M5ffo0ISEh7Ny5k/DwcK6//noGDhxIixYtmDlzJgC7d++mT58+dOnShT59+pA0I9qMGTNo164dYWFhvPLKK0ybNo29e/cSGhrKmDFjiIuLY9iwYYSFhdGhQwd++80ZoHXx4sW0aNGCfv36sW7dunTxHjhwgE6dOhEWFkZoaCjHjx8nNjaWW2+9la5du9KlSxe2bdsGwBNPPEFYWBjXXnstkyZNAuCNN95g7ty5hIaGEhERwahRowgJCSEsLIwZM2Zc9O9x7dq1dOzYEYA+ffqwePHidGV+++03qlevnurD/csvv6Rs2bL06NGDAQMGsGfPHgCqVKlSoO8GAA7GnuH+9XOI9/Pn/av6ItigcqZwEGdcOS8FRNap6jVZrcstwcHBunr16gy3LViwgDlz5jBx4sTkD/21a9dy+PBhevfuTVBQEP3792fQoEGEh4fz4IMP8vvvv3PmzBmCg4PZvHkzQ4YMYeTIkbRr146vv/6aJUuWMHr0aEJDQ1mxYgVly5YlISEBf39/goKCkj+kJ06cyLFjx3jyySc5ePAgN910E8uWLSM4OJhZD7WhTsJfXP/GKoa0rcldHS58cM6KOMDqv2IZO7AxSX+L0V9uoUXd8gxuW5N1u47z4rfb+PL/ruXUuXjKlizGubgEmj+7hA0vdmLZthg+/XUvHwx1Rvxo9vRi1r3QgWL+fiQmKn5+FyaKO3M+gV7/WZXu99avRTUevT71FBNXjA7nz5dDAfh4yW4OHD/H6D5BqfebsJqP776af87YxLCOdehwRWXumxLJ0VNxfHF/S+auO8SnK/YxfUTL5H2Cngxn27hQl3/tfGCpk8g2BZXn1NbTlF1+mjMtSnH+ipI0CCzLZQEXkttQOQjAx3pZnoRqclH15tBrXF5HkS0iEqGqwRltczPWUIKIXK6q2z0Hawgk5GSAOSUyMpJffvmFUM9okefOnePIkSNUrVqVHj16MHv2bD7//PPk8i1btqR48eIUL16catWqER0dTWRkJE8++SQA8fHxBAUFsX37dq6++mrKli0LgL+/f4bnXr58OQsWLAAgNjYWgOPHj1O3Wnk4ILRpUCHdfn2ursq63ce5Y9Ja6lQuxQv9ryBy7wl++fMoE8N3AVDM82H+7qJdzFlzEH8/4dDx8xw6cT7d8cbd3Ji7P4rEzw8e69mQZrUuPAJSuoQ/4U+0c/W79JMLCST2TDyVy5ZItX3uukME169AlXKp11cuW5zWDSoiIlx/VVWe+HKLq/PlWx1qc+JcHLG7YglYe4aECn6cDyqBv0iqJGBMQeYmETwGLBKRHTgDztUDhvo0qovUrFkzevTowX//+18Azp8/T4kSJfjjjz9Yvnw5/fr1Y8KECTz88MOAU/0RHx/PmTNnOHjwIIGBgTRr1ozRo0fTsmXL5GOcPHmSyMhIzpw5Q+nSpUlMTMTPz49ixYol/9ysWTOCgoL4xz/+kbwfQEBAAHuaP0jtXrVZNf16gjreBnfdlRxzwunTvDDc6XY4bNgwvq9+I82uX0JISAgDBgxIPlbMqVN89Np1RG6OJi4ujsaNG6O3TqHErl3EH5wEQyejqnQ7e5a+pUuzdOlSnv3Pf/jq6a+Sz3XmzBl69Uo/dFS/fv149NHUD49fs2AQyxs/Qvv27Zk//XrefPNNaHKhGmTtmDGEr/+J5TMSiNypbI5XZox4h9Cam5k9ezbDhk4kYsUKLm81FoamaMMYEwRD517MnzdvHD7MhLkbiYucwaDTP/PPjg+wMb4B/a+qSevBLVOXXeD5b9Hz49yP05hLoapZvnDmKL4az3zFbvbx1atVq1bqzdNPP62dO3fW0NBQveOOO/T06dMaEhKi27dv17i4OA0LC9M1a9bookWLtEePHtq/f3+95pprdPr06aqqumvXLu3bt6+GhYVpWFiYfvLJJ6qq+vnnn2ubNm00NDRUx40bp6qq//rXv7Rnz5763//+V8+fP6/33XefhoaGamhoqI4aNUpVVRctWqQtWrTQ3r1768CBA/Xjjz9OFe/cuXP1uuuu086dO2uPHj306NGjeuzYMR08eLCGhYVpaGiovv7665qYmKg333yztmvXTocOHaotW7bU3bt36/Hjx7VDhw46cOBAXb9+vXbu3Fk7d+6sbdu21blz53r9XXmzfft2DQ0N1fbt2+uLL76YvH7IkCHpyv7973/XJUuWqKpqYmKijhw5Ujt37qwdO3bUTZs2qarq4sWLtWvXrlq6dGnt2rWrfvXVVxcdW67q3FnXXX61rruymb7Z/Q6t98R32valHzMsetf8u/Su+XflcoDGuAOs1kw+V7NsIwAQkfZAfVLcQajqVF8kpqx4ayPIjvDwcD799FM++OCDHIjKFFYHWrYj+uRxzpQqxb3dHie2ZABD2tZlbAbzEA/13BF8bHcEJh+6pDYCEfkEuBxYy4W2AQXyJBEYk5uiT56imL8ypWkvYks67S02xLQpbNy0EQQDTdXNrUMBEhoamtyobExGIjbuoZTEE58A8+s7jew2Gb0pjNw8R/AHUN3XgRiT30SNfxMROBvnT6L42XMDptByc0cQCGwUkd+Ac0krVbWfz6LyFTd3ADfcAKNGXSh/113O6/BhuPnmrPdPW/6f/4S+fWHLFrjvvqz3T1t+7Fho3x6WL4ennsp6/7Tl33sPGjeGb7+F8eOz3j9t+S+/hMBAmDzZeWUlbXnPYG28/jp8913W+6cs/+uv8JWn19Po0c6yN1WqpC5/5Ah4Hrxj+HD480/v+19xRXL5+FsH0fGnnwgPCuaX6s4jM3Y3YAorN4ngeV8HYUx+oqocXL4S/IvzwnX3cLKE07036DKblsMUTpn2GhIRyapdwE2ZnJZTvYaMyczaDz6j5Osv8Z+WtxJZqQEA+8tX5av723u9I7BeQyY/89ZryFsbwSLPAHOpRtQSkRIi0kVEpgB/z8lAjclrCbGxxL39XzZWrsePdYP5z3fj+c9342lSPcCqhUyh5a1qqCdwN/C5iDQAjgGlAH/gB+A/qrrW1wEak5s2vPQqpc+c5O22w1C58D3ppQyeGzCmsPA2VeVZPCOOeoadDgTOqOqxXIrNmFx1JvIP/L+dzTcNr2NHxVrJ62tWLE1duxswhZibxmJUNQ7Y7+NYjMkzmpDA74+ORkuW45Mm1yevL1HMj7o2BaUp5GyGMmOAYzO/pNLubXxwVV9OFy+dvL5WRUsCpvBzdUdgTGEWsW4HOvZVtgVezqLaF0YUrVe5DJe94OLZDWMKOEsEpkibtnIXh555ga5xZ3nn6gGQYh6GNwa1AGsbMEWAmzmLbxKRrSISKyLHReSEiBx3c3AR6SkiW0Rkm4g86aVcaxFJEBEXj+4akzMiomL45INvuX7Xb8y+vBO7yl8YSSX5KeItW5yXMYWYmzuCV4G+qropOwcWEX/gbaA7sAdYJSLfqOrGDMq9AnyfneMbc6kemx7Bo+tmEV26AtOu7J68PtWYQknDgiQNfWFMIeSmsfhgdpOARxtgm6ruUNXzwHTgxgzKPQR8BRy6iHMYc1Hu/HAlV61eyOXH9zHpqn6cLVYSgPpVyvBlFk8QG1PYuLkjWC0iM4A5pB50blYW+9UCdqdY3gO0TVlARGoBA4AuQOvMDiQiw4HhAHXr1s2smDGudB8fzuHd+3l/0/dEVLuCpTWvBqBTo0Cm3tM2i72NKXzcJILywGmgR4p1CmSVCCSDdWnHJXoTeEJVE0QyKu7ZSXUSMAmcsYayOK8xmeo+Ppyt0ad47I/vKJEYl9xAXK9yGUsCpsjKMhGo6sVOVL8HqJNiuTawL02ZYGC6JwkEAr1FJF5V51zkOY3J1Mjpv7M1+hTNo7fRZc8apl3RjX3lqiJ4eggZU0S5maqyNvA/4Dqcb/RLgUdUdU8Wu64CGnnGKdoLDAaGpCygqg1SnGcy8J0lAeMLEVExzFm7D//EBP5v/WwOlKnEF1d0oUrZ4ky6s3XmbQJPP527gRqTB9xUDX0MTANu8Szf4VnXPdM9AFWNF5EHcXoD+QMfqeoGERnh2T7xoqM2Jpv++cVaAPpvX0y9Ewd5vu1Q6taoxI//DPW+Y7duPo/NmLzmJhFUVdWUA6xPFpGRbg6uqvOAeWnWZZgAVPUuN8c0Jrvu/HAlfx05TeCZY9y++UdWVG/K9kYtWZ1VEgBYu9Z5b9HChxEak7fcJILDInIH8Lln+TbgiO9CMibnTFu5i8VbDwMwPPIb/DSRic1v5NHujd0dYORI592eIzCFmJvnCO4GbgUO4IxAerNnnTH53kfLdgJw7aEtdNy3numNu9G4RWOGtLVuyMYkcXNHcKhATlRviryIqBi2HTpJ8YR47l83mz1lA1nasgdLrZuoMam4SQR/iMhBYAmwGFimqrG+DcuYS/feL9sBGLgtnNqnDvOv9vfSqXntPI7KmPwny6ohVQ3CaReIBG4A1onIWh/HZcwliYiK4YeNB7ns1BEGb1nIkppX83u1xgy81hKBMWm5fY7gOqAjcA2wAedZAmPyrWfmRAIwIvJrEsWPSc370a3pZdkfQ2jsWB9EZ0z+4qZqaBfOw2FjVXWEj+Mx5pJNW7mLjftP0Hb/Btod2MgHzfpwuHRFRnS+PPsHa98+5wM0Jp9x02uoJTAVGCIiv4rIVBG5x8dxGXPR3lm0lZLx5xkROYeogMuYc3knRnRqeHEjii5f7ryMKcTcjDW0TkS2A9txqofuADoBH/o4NmOybdrKXew5dpa//fkz1U/H8Ph1I6gdGMCTvZtc3AGf8kxVac8RmELMTRvBaqAksBynbaCTqkb5OjBjLsZHy3ZS82Q0t2xbxM+1ryWyahBDGgXmdVjG5Gtu2gh6qWq0zyMx5hJFRMWw/eAJXlw/m/N+xfngqhsQsJ5CxmTBTfdRSwKmQJi1Zg/X7VtPq0N/MrXJ9cSUKs+YAc1ttjFjsuCmsdiYAiFqdzT3RX7D9go1+a5Be9rUr2RDSRjjgpuqIWPyvYioGBr/OJPAs7GMafM3Ev38Cbos4NIP/Oabl34MY/K5LO8IROQWEQnw/Py0iMwSkWt9H5ox7v00/1f6b1vM93XbsLlyffwkh9oGWrSwIahNoeemaugZVT0hIh2A64EpwLu+DcsY91SVJjPe40yxknzcrDcAwfUq5UzbwMKFzsuYQsxNIkjwvPcB3lXVr4ESvgvJmOxZ8OYUgvb/ycdNexNbshxAzlQLAbz0kvMyphBzkwj2ish7OHMSzBORki73M8bnEk6coOKUd9hSsQ7f178wvLR1GTXGPTcf6LfizDvcU1WPAZWBx3wZlDFuLX1qLOXPnuTta24iUZx/zrUqlrIuo8Zkg5tE8J6qzlLVrQCquh/4m2/DMiZrZzdtInDh18xr0I6tleokr/+/sEZ5GJUxBY+b7qPNUi6IiD/QyjfhGOOOJiYS+di/iC9ehilNeiWvb1I9wJ4dMCabMk0EIjIaeAooLSLHk1YD54FJuRCbMZmKnT2bcts2Mb7lIE6WKJO8/qUBzXP2RO+9l7PHMyYfyjQRqOrLwMsi8rKqjs7FmIzxKuHYMf4a+yrbKtfnp7oXbk5b18+hLqMpNW6cs8czJh9yMwz1aBGpBDQCSqVYv9iXgRmTmQ3/foVip0/yVpthqFxo5nqy10UONe3Nt98673375vyxjckn3AxDPQx4BKgNrAXaAb8CXXwamTEZOBMZSbH5XzOnYQf+qlAzeb1P7gYAxo933i0RmELMTa+hR4DWQJSqhuHMWGYjkppcpwkJHHj+BU6UKc+nV16faptP7gaMKSLcJIKzqnoWQERKqupmwCpOTa479sUXnN2wgXeb3MDp4sm1lLTx1d2AMUWEm+6je0SkIjAH+FFEYoB9vgzKmLTijxzh0H/e5EDDZoTXapFqW44NJ2FMEeWmsXiA58fnRWQRUAFY4NOojEnj0OvjSTxzhpkdBsNxSV5vM5AZc+lczUfgGXm0kap+LCJVgVrATp9GZozH6TVriJ09m7hb72DBidKptnVreplvq4U++cR3xzYmn3AzH8FzwBNA0rMExYFP3RxcRHqKyBYR2SYiT2aw/XYRWe95LReRa7ITvCn8ND6eAy/8m2I1ajCxbmcS9cI2P4ERnS/3bQB16jgvYwoxN43FA4B+wCkAVd0HZFkp6xmK4m2gF9AUuE1EmqYpthPorKpXAy9iTyybNGI++4xzW7Zw6t6HmbctNtW2HJtzwJsZM5yXMYWYm6qh86qqIqIAIlLW5bHbANtUdYdnv+nAjcDGpAKqujxF+RU4zyoYA0DcwUNET/gfZTt15P495YEzqbbnSiPxu545mAYN8v25jMkjbu4IvvDMR1BRRO4FFgLvu9ivFrA7xfIez7rM3APMz2iDiAwXkdUisjo62h5hKCoOvfoqGhfH8/V68tfRM+m2WyOxMTnDTa+h10WkO3Ac5/mBZ1X1RxfHlgzWaQbrEJEwnETQIZMYJuGpNgoODs7wGKZwObViBcfnzmVh6778fKJkuu0+e5LYmCLIzRATZYGfVfVHEWkMNBaR4qoal8Wue4CUrWy1yeD5AxG5GvgA6KWqR9yHbgorPX+ev555nuhyVfhf9fbptgv2JLExOclN1dBioKSI1MKpFhoKTHax3yqgkYg0EJESwGDgm5QFRKQuMAv4m6r+mZ3ATeE1+dGX8dsdxdtX9ee8f/FU22qUL8mX97e3uwFjcpCbxmJR1dMicg/wP1V9VUR+z2onVY0XkQdxprn0Bz5S1Q0iMsKzfSLwLFAFeEdEAOJVNfhiL8YUfH9/+WtGLvqK5TWuYlX11N/6W9SuwJwHM6w99J0vv8zd8xmTB1wlAhEJAW7Hqcd3ux+qOg+Yl2bdxBQ/DwOGuQvVFHYjp/9Ohx8+A+C95v1SbWtUtWzuJwGAwMDcP6cxuczt6KOjgdmeb/QNgUW+DcsUNRFRMez+/mc67I/k88bdOFSmcvK2FrUr8OM/Q/MmsMmTnZcxhZibXkOLcdoJkpZ3AA/7MihT9DwzJ5Kb/lrB7nJVmRXUGYAS/sLz/a7K2zmIk5LAXXflXQzG+JirKh5jfGncvE1s3H+Cza3/RuDZWOL9nH+Wnw8PsUZhY3KBm6ohY3wmIiqGiYt3AJDo559cJTSiU0NLAsbkEjeDzllrmfGZZ+ZEplvXpHoAT/a25wSMyS2ZJgIR6Ssi0UCkiOwRkfRP9hhzCZKqhNJ6aUDzPIjGmKLLWxvBGKCjqm4WkbbAq0Dn3AnLFHYpq4RSyndVQvPmZV3GmALOWyKI98xPjKquFBGbD9DkmIyqhALLlch/VUJlyuR1BMb4nLdEUE1EHs1sWVXf8F1YpjDLrEro0e6N8yCaLLzzjvP+wAN5G4cxPuQtEbxP6glo0i4bk22ZVQn1b1Ezb58XyMwXXzjvlghMIZZpIlDVF3IzEFM0ZNZL6M3BLfMgGmMMZNF9VER6ichiETksItEi8ouI9M6t4EzhMm3lLuslZEw+lOkdgWc2svuAx4HVntXBwDgRqe2ZLMYY1z5atjPdOptgxpi8562N4B9AB1U9mmLdzyLSC1iKTTRvsiEiKobth06mW28TzBiT97wlAkmTBABQ1SOeuQOMcW3Wmj3p5ikdO6B5/r8bCA93XfTKylf6Lg5jfMhbIjguIteo6rqUK0XkGiB9Ra8xXkSfOJdquU39Svmzl9AleKLNE3kdgjEXxVtj8T+Bb0Tkec9wEzeIyAvA18CjXvYzJktBlxW8nsh//fUX3bp1S7UuKCgIgMmTJ9OgQQNCQ0Np27YtI0aMIDY21tVxJ0yYkCNlvFm7di2vvfZauvUvvfQSk30w30J4eDjr16/P8eMCLFiwgJCQEEJCQvj+++8zLffRRx9RvPiFqU4TEhIYNWoU3bp1IzQ0lI0bNwLQo0cPQkNDCQ0NpXTp0kRGpu/ZVthlmghUdSnQ1lPmLuBuz8/tPNuMcSUiKoaftxxKXi7mLwy8tnYeRuQb99xzD+Hh4axcuZLGjRvzyCOPuNovNxJBixYteOyxxy7pGNnhq0SQkJDA448/zvz585k/fz6PPfYYCQkJ6cqdPXuWWbNmUadOneR1kyZN4oorrmDhwoWEh4fTtGlTAH744QfCw8OZPn06l19+Oc2bF71ebF67j6rqAVV9VlUHqupNqvqMqh7IreBM4TBrzR7iEy60EHRpXC3/tw1con/84x8sWbKExMTEVOtHjRpFSEgIYWFhzJgxgzfeeIO9e/cSGhrKhx9+yKJFiwgLC6Njx47ceOONnD17lmnTpiWXGTNmDHFxcQwbNoywsDA6dOjAb7/9xrlz5wgJCQHgf//7X/LPr732GtOmTSM8PJxhw5xZYRcvXkyLFi3o168f69ZdqPmdOXMmHTt2pEOHDvz73/9Od00hISEcPnwYgGXLlnGXZ7KeF154gZCQENq2bcvcuXM5evQokydPZsyYMYSGhpKQkJDlsd3aunUrDRo0oGLFilSsWJEGDRqwffv2dOUmTJjAiBEj8PO78BE3c+ZMoqKiCAsL48EHH+T8+fOp9pk2bRqDBw++6NgKMm/dR28Eaqvq257llUBVz+YnVHVmLsRnCoGtBwtPk1JERAShoaGuylatWpXDhw9TrVq15HXz589n3bp1FCtWjMTERPz8/HjnnXcI9zRKnzp1ikWLnJlgn3jiCb744gvuvPNOnn322eQyEydOJCgoiA8++ICDBw9y0003sWzZMsqVK0d0dDRLliyhatWqxMbGsmjRIj788EO2bNmSHMOjjz7KN998Q506dbj++usBiImJYfz48SxZsoTixYszYMAAIiMjU307Hjx4MJ9//jkPPfQQn3zyCXfeeSdr165lyZIlLF++nNjYWNq0acPmzZu56667CAoK4o477nB17F9//ZXRo0en+x0+++yzdOnSJXn56NGjVKp04UtExYoVOXLkSKp9YmJiWLx4MY8//jgjR45MXr93715q1KjBokWLGDVqFB999BEjRoxI3v7ZZ58xc2bR/Fjz1lj8OJAyPZYEWgNlgY+BovkbM9kSERXD6qiYVOsCA0rmUTSXrlWrVixcuDB5OamNICPR0dEEBqaezmPcuHHcfffd+Pn58dhjj9GsWbNU2zds2MDTTz/NuXPnOHjwIOXLl0933MjISJYvX86CBQsAktsiwsLC+Omnnzhz5gx9+/blp59+Ijo6mho1aqRKBMePH6duXaehvk2bNgBs27aNqKgounfvDsCxY8eIiopK9WE9ZMgQ+vfvz3333ceKFSt49913+eKLL2jXrh0iQsWKFalWrVryXUMSN8cOCQlJTnTeVK5cmWPHjiUvx8bGUrly5VRlXn75ZR5//PEM9+3ZsycAPXv2ZNasWcnbNm3aROnSpWnYsGGWMRRG3hJBCVXdnWJ5qaoeAY6ISFkfx2UKiVlr9pCYot+on1Ao2wfSmjBhAtddd12qqglVpVu3bvTt25elS5fy7LPP8tVXX6UqM2bMmOSqlscffxxV55eX8g6iWbNmBAUF8Y9//AMguYqjS5cuPPLII/To0YMuXbpw++2307p163SxBQQEsGfPHmrXrs2qVasICgqiYcOGBAUFsXDhwuRzJZ07SdWqVQkMDOTVV1+lT58+iAiNGzfm/fffR1WJjY3l0KFDBAYGUqJECeLj4wFcHdvtHUGjRo3YuXMnx48fB2Dnzp3pkvGff/7J2LFjGTt2LPv372fQoEHMmDGD0NBQVq9eTVBQUPJ7kk8++YTbb789sz9noectEaSqxFXVB1MsVsUYF9J2Gw2uV3ifJP7www9ZuHAhZ86c4eqrr07XwBsfH0+vXr0ApzHz2WefBZxvwwMGDGDQoEEMHjyYe+65h8aNG1OhQoXkO4Kbb76ZPn360KtXL+6//34eeughwsLCAAgODua1116jdevWbN68mXHjxhEUFMSBAwdSfYgmGT9+PH379qVmzZoEBDi9t6pUqcLIkSPp0qUL/v7+FC9enKlTp1K9evVU+955550MHjyYP/74A3Aaodu3b09ISAiJiYmMHz8ePz8/unfvzsiRI/nuu+/44osvsjy22zsCf39/Xn755eQqrZdffhl/f38OHDjAa6+9xvjx45kzZ05y+aCgIGbMmAHA448/ztChQ5k4cSKVK1fmk08+AZwE/eWXX/Lrr79mef7CStJm5uQNIp8B4ar6fpr19wGhqnpbLsSXTnBwsK5evTrrgiZfuHXicn7760LVUI+mlzHpzuA8jMiYoklEIlQ1w/98WQ0xMUdEhgBrPOta4bQV9M/RCE2hVNjaB4wprLwNQ30IaC8iXYCkFq25qvpzrkRmCryi2j5gTEHj7Y4AAM8Hv334m2xbk+ZuoDC3DxhTkHl9oMyYizVt5S42HUj9/EBBHFbCmKLAEoHxiYzmHrBqIWPyJ0sEJsdFRMWwLc3cAzYBjTH5lyUCk+Pe+yX92C82AY0x+ZdPE4GI9BSRLSKyTUSezGC7iMgEz/b1InKtL+MxvhcRFcPiP6NTratVsZTdDRiTj2XZa+hiiYg/8DbQHdgDrBKRb1R1Y4pivYBGnldb4F3Pe46LiIph3PxNbNgby7mERFSd7owiQqJq8rJCrm7L6/PnZNyJiaSbhQygac0KvviTGmNyiM8SAdAG2KaqOwBEZDpwI5AyEdwITFXn8eYVIlJRRGqo6v6cDCQiKoZbJy4nIc2nlNPHXdMs5/a2vD5/zsadkRGdL/dewBiTp3xZNVQLSDlo3R7PuuyWQUSGi8hqEVkdHR2ddnOWVuw4ki4JmNzRv0VNqxYyJp/zZSLIaIb7tB/HbsqgqpNUNVhVg6tWzf54d+0aVsE/ozMZn+rUKJA3B7fM6zCMMVnwZdXQHqBOiuXawL6LKHPJWtWrxBcj2lsbQS7EXcxPqBZQkgfCGhW6yemNKax8mQhWAY1EpAGwF2eSmyFpynwDPOhpP2gLxOZ0+0CSVvUqMXNEe18c2hhjCjSfJQJVjReRB4HvAX/gI1XdICIjPNsnAvOA3sA24DQw1FfxGGOMyZgv7whQ1Xk4H/Yp101M8bMC/+fLGIwxxnhnTxYbY0wRZ4nAGGOKOEsExhhTxFkiMMaYIi7TyevzKxGJBqIucvdA4HAOhlMQ2DUXDXbNRcOlXHM9Vc3widwClwguhYisVtXgvI4jN9k1Fw12zUWDr67ZqoaMMaaIs0RgjDFFXFFLBJPyOoA8YNdcNNg1Fw0+ueYi1UZgjDEmvaJ2R2CMMSYNSwTGGFPEFcpEICI9RWSLiGwTkScz2C4iMsGzfb2IXJsXceYkF9d8u+da14vIchG5Ji/izElZXXOKcq1FJEFEbs7N+HzBzTWLSKiIrBWRDSLyS27HmNNc/NuuICLfisg6zzUX6FGMReQjETkkIn9ksj3nP79UtVC9cIa83g40BEoA64Cmacr0BubjzJDWDliZ13HnwjW3Byp5fu5VFK45RbmfcUbBvTmv486Fv3NFnHnB63qWq+V13LlwzU8Br3h+rgocBUrkdeyXcM2dgGuBPzLZnuOfX4XxjqANsE1Vd6jqeWA6cGOaMjcCU9WxAqgoIjVyO9AclOU1q+pyVY3xLK7AmQ2uIHPzdwZ4CPgKOJSbwfmIm2seAsxS1V0AqlrQr9vNNSsQICIClMNJBPG5G2bOUdXFONeQmRz//CqMiaAWsDvF8h7PuuyWKUiyez334HyjKMiyvGYRqQUMACZSOLj5O18BVBKRcBGJEJE7cy0633BzzW8BTXCmuY0EHlHVxNwJL0/k+OeXTyemySMZTVOfto+smzIFievrEZEwnETQwacR+Z6ba34TeEJVE5wviwWem2suBrQCugKlgV9FZIWq/unr4HzEzTVfD6wFugCXAz+KyBJVPe7j2PJKjn9+FcZEsAeok2K5Ns43heyWKUhcXY+IXA18APRS1SO5FJuvuLnmYGC6JwkEAr1FJF5V5+RKhDnP7b/tw6p6CjglIouBa4CCmgjcXPNQYJw6FejbRGQncCXwW+6EmOty/POrMFYNrQIaiUgDESkBDAa+SVPmG+BOT+t7OyBWVffndqA5KMtrFpG6wCzgbwX422FKWV6zqjZQ1fqqWh/4EnigACcBcPdv+2ugo4gUE5EyQFtgUy7HmZPcXPMunDsgROQyoDGwI1ejzF05/vlV6O4IVDVeRB4EvsfpcfCRqm4QkRGe7RNxepD0BrYBp3G+URRYLq/5WaAK8I7nG3K8FuCRG11ec6Hi5ppVdZOILADWA4nAB6qaYTfEgsDl3/lFYLKIROJUmzyhqgV2eGoR+RwIBQJFZA/wHFAcfPf5ZUNMGGNMEVcYq4aMMcZkgyUCY4wp4iwRGGNMEWeJwBhjijhLBMYYU8RZIjBeichIT3/0HCnn4jj/FpFuGawPFZHvLvX4Xs7bQkR6++jYk0Vkp2dE0M0i8lyKbR+ISFNfnDdNDA96RqtUEQlMsT7TkSwzG/VTRCqLyI8istXzXimD89X3nOvFFOsCRSRORN7yLD8vIqdFpFqKMid9cf3GO0sEJisjATcf8G7LeaWqz6rqwks9zkVogdM321ceU9UWnvP8XUQaAKjqMFXd6MPzJlkGdAOi0qzvBTTyvIYD7wKIiD/wtmd7U+C2FAnrSeAnVW0E/ORZzsgO4IYUy7cAG9KUOQz88yKux+QgSwQGABEpKyJzxRnT/Q8RGSQiDwM1gUUisshT7l0RWS3OuO8veNZlVK6HiPwqImtEZKaIlBORNiIyy7P9RhE5IyIlRKSUiOzwrJ8snnkDPN9IN4vIUuCmNLF+JCKrROR3EUk36qiIzEj5Dd9z3IGec30sIpGefcM8T6z+Gxjk+dY+yM050pyvvohsEpH3Pb+bH0SkdAZFS3neT3n2CxeRYM/PJ0VkjOdvsEKcp2QRkVs8f5N14gwZkW2q+ruq/pXBpsxGsvQ26ueNwBTPz1OA/pmc9gywKen6gEHAF2nKfITze698EZdlcoglApOkJ7BPVa9R1auABao6AWcMkzBVDfOU+5fnieSrgc4icnXacp6qh6eBbqp6LbAaeBRYA7T0HKcj8AfQGmcYhJUpgxGRUsD7QF9P2eopNv8L+FlVWwNhwGsiUjbN9UzH+eDB80HfFeeJzP8DUNXmwG04H2R+OE9ez1DVFqo6w+U50moEvK2qzYBjwMAU214TkbU448RMz2R46LLAClW9BlgM3OtZ/yxwvWd9v7Q7iUiAJ4Fl9Mqq2imzkSy9jXB5WdKQBp73amRuOjBYRGoDCaQfE+ckTjJ4JIs4jQ9ZIjBJIoFuIvKKiHRU1dhMyt0qImuA34FmONUGabXzrF/m+fD7O1BPVeNxBgVrgvON8w2cSTg6AkvSHONKYKeqbvUMJvZpim09gCc9xw7H+ZZdN83+84EuIlISp3pjsaqewRl19RMAVd2MU1VyRQbX4OYcae1U1bWenyOA+im2JVUNVQe6ikj7DPY/DyS1g6TcfxnOEAr34gyzkIqqnvAksIxeWVU7ZTaSZU6NcLkA6I6TdGdkUmYCTnVZ+Ys4vskBhW6sIXNxVPVPEWmFU0/+soj8oKr/TlnGU689CmitqjEiMpkLVR2pigI/quptGWxbgvPBHAcsBCbjfLiNyiisTMIVYKCqbvFyPWdFJBxniOJBwOcp9nUjy3Nk4FyKnxNwhoFOG9dJT1wdgOVpNsfphTFfEvD8/1TVESLSFugDrBWRFilHjxWRANIn0iRDskgGmY1kWSKT9QAHRaSGqu73VCNlOvmNqp4XkQicdoBmOHd4acscE5FpwANe4jQ+ZHcEBgARqQmcVtVPgddxpsoDOAEEeH4uj1O3Heupv+6V4hApy60ArhORIM+xy4hI0rfuxTgNy7+qajTOQHhXkr4RcTPQQEQu9yynTCrfAw+JOKPniUhLMjYdZ0Cujp59ks5/u2e/K3C+5W9JE3+m5xCRWiLyUybny5KIFMOpCtuejX0uV9WVqvosTuNqyg/oS70jyGwkS2+jfn6Dc5eH5/1rT5yZ/W7G4wwE523o8zeA+7Avp3nCEoFJ0hz4zVMV8i/gJc/6ScB8EVmkqutwqoQ24NTrLkuxf8py0cBdwOcish4nMVzpKbcSuAznAxmcUTLXp/gmDDjf6HF6scz1NBan7O3yIs5ojOvFmeD7RTL2A07V00JPgyfAO4C/OCNVzgDuUtVzwCKgaVJjsZdz1ODipkFMaiNYj1MNNyub+0Z64liMM29vtojIw+KMZFkb55o+8Gyah9O7ZxtOm8wD4Iz6CSSN+rkJ+EJVk5L1OKC7iGzFqfYZ51mf4e9GVTeo6pS069OUOQzMBkpm99rMpbPRR43JBnGGRN6lqmnHxC/y7HdTcFkiMMaYIs6qhowxpoizRGCMMUWcJQJjjCniLBEYY0wRZ4nAGGOKOEsExhhTxP0/5kfwQi7LpdYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 2.544 2.517\n",
      "fractional expected GOP seats =  3.761  out of  8 seats for MN\n",
      "simpler expected GOP seats =  3.6893  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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